Microbial-mediated mechanisms of iron-bound organic carbon formation in mangrove rhizosphere soils: An integrated biogeochemical and metagenomic analysis along a salinity gradient

Microbial-mediated mechanisms of iron-bound organic carbon formation in mangrove rhizosphere soils: An integrated biogeochemical and metagenomic analysis along a salinity gradient | 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 Microbial-mediated mechanisms of iron-bound organic carbon formation in mangrove rhizosphere soils: An integrated biogeochemical and metagenomic analysis along a salinity gradient Ying Lei, Lv Gong, Xiuzhen Li, Zhongzheng Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8936246/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims Mangrove ecosystems serve as critical blue carbon sinks, yet the microbial mechanisms governing iron-bound organic carbon (Fe-OC) formation under varying salinity conditions remain poorly understood. This study aims to elucidate the microbial-mediated pathways controlling Fe-OC formation in Aegiceras corniculatum rhizosphere soils across a natural salinity gradient (1.5–2.4‰). Methods We employed an integrated biogeochemical and shotgun metagenomic approach to analyze rhizosphere and bulk soils from the Nanliu River Estuary, China. Structural equation modeling (SEM) was utilized to identify the dominant mechanistic pathways linking environmental factors, microbial metabolism, and Fe-OC formation. Results Under increasing salt stress, absolute Fe-OC content declined from 15.2 to 4.0 mg·g⁻¹, yet its proportion within total organic carbon increased from 34.2% to 61.8%. Metagenomic analysis revealed stable functional gene diversity despite significant taxonomic turnover, demonstrating inherent functional redundancy. Iron oxidation genes were enriched in saline flats, while carbon fixation genes concentrated in fresher sites. SEM identified salinity as the master environmental control (R² = 0.67), operating primarily through a dominant pathway: salinity → iron oxidation genes → iron oxides → Fe-OC. Conclusion Mangrove root metabolism and microbial functional genes synergistically mediate soil iron-carbon binding. Salinity acts as the primary environmental control through both direct geochemical effects and indirect pathways via microbial community restructuring, highlighting the importance of functional redundancy in maintaining ecosystem resilience under environmental stress. Nanliu River mangrove salinity iron-bound organic carbon metagenomics structural equation modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Mangrove ecosystems occupy the dynamic interface between terrestrial and marine environments, where prolonged seawater residence times create complex mixing zones with steep salinity gradients, high ionic strength, and dramatic redox variations (Dagg et al., 2004 ; Bauer et al., 2013 ). These physicochemical conditions make estuarine regions biogeochemical hotspots where processes such as adsorption, decomposition, and sedimentation operate at enhanced rates. Carbon dynamics in mangrove soils exhibit significant spatial heterogeneity driven by tidal fluctuations, salinity variations, and nutrient availability, resulting in complex patterns of carbon burial that remain incompletely understood (McLeod et al., 2011 ). Recent advances in coastal carbon research have revealed that iron-bound organic carbon (Fe-OC) represents a substantial and potentially vulnerable component of soil carbon pools in mangrove systems (Dicen et al., 2019 ). Estimates suggest that Fe-OC comprises 8–40% of total organic carbon in mangrove sediments, with this proportion varying significantly across environmental gradients (Dicen et al., 2019 ; Ruiz et al., 2024 ). The formation and stability of Fe-OC depend on sophisticated interactions between iron minerals and organic matter through multiple binding mechanisms, including coordination exchange, cation bridging, and van der Waals forces, with amorphous iron oxides proving particularly effective at carbon stabilization. Research in Amazon estuary mangroves has demonstrated that iron-mediated organo-mineral interactions can stabilize approximately 8% of soil organic carbon (SOC), with approximately 15% of SOC directly binding with reactive iron through coordination exchange (Ruiz et al., 2024 ; Kida and Fujitake, 2020 ). These iron-mediated preservation mechanisms operate through adsorption, co-precipitation, and encapsulation processes that can maintain organic matter stability over millennial timescales (Lalonde et al., 2012 ). However, emerging evidence suggests that salinity gradients and climate change may fundamentally alter these iron-carbon interactions through complex biogeochemical pathways that remain poorly characterized. While the importance of Fe-OC has been recognized, the biological mechanisms governing its formation and stability remain largely unexplored. Recent breakthroughs in molecular ecology have revealed that microbial communities serve as the primary drivers of biogeochemical processes in coastal wetlands, mediating critical reactions through specific functional genes that regulate carbon decomposition, carbon fixation, iron oxidation, and iron reduction (Zhang et al., 2025 ; Dong et al., 2024 ). Iron oxidation processes are primarily driven by microaerophilic iron-oxidizing bacteria (FeOB) such as Sideroxydans lithotrophicus and Gallionella capsiferriformans , which oxidize Fe(II) to Fe(III) under micro-oxic conditions, forming reactive iron oxides that provide the mineral matrix for carbon stabilization (Dong et al., 2024 ). Simultaneously, these bacteria often possess carbon fixation genes (cbbL, cbbM) that encode RubisCO enzymes participating in the Calvin-Benson-Bassham pathway, creating a synergistic coupling between iron oxidation and carbon fixation processes (Dong et al., 2024 ). Conversely, iron reduction is mediated by anaerobic bacteria expressing functional genes such as MtrABCD that facilitate Fe(III) reduction to Fe(II), using organic carbon as electron donors and potentially destabilizing existing Fe-OC complexes. Carbon decomposition genes (bamA, alkB, and oah) drive the breakdown of organic matter, while carbon fixation genes contribute to primary productivity and organic matter input. The intricate interactions among these functional gene networks create complex biogeochemical webs that fundamentally control carbon sequestration processes, yet their responses to environmental gradients in mangrove systems remain uncharacterized. Salinity emerges as a potentially critical environmental control that may orchestrate Fe-OC dynamics through multiple simultaneous pathways. Direct geochemical effects include competition between seawater cations (Na⁺, Mg²⁺, Ca²⁺) and organic matter for binding sites on iron mineral surfaces, potentially reducing carbon adsorption capacity (Tomaszewski et al., 2021 ). High ionic strength can enhance microbial iron reduction activity, destabilizing existing Fe-OC complexes, while sulfidization processes under saline conditions may remove reactive iron from the carbon stabilization pool through the formation of iron sulfides (Liu et al., 2021 ). Beyond these direct effects, salinity may exert profound indirect control through restructuring microbial communities and altering functional gene expression patterns. Osmotic stress can suppress carbon fixation enzymes while promoting decomposition processes, fundamentally shifting the balance between carbon input and output. However, the relative importance of direct versus microbially mediated pathways and the specific salinity thresholds at which these mechanisms operate remain unresolved. Mangrove rhizospheres represent unique biogeochemical interfaces where plant-soil-microorganism interactions intensify iron-carbon coupling processes. Root systems create localized oxidizing zones through radial oxygen loss while simultaneously releasing organic exudates that serve as carbon sources and electron donors for microbial metabolism (Duan et al., 2020 ). Under salt stress, root exudate composition changes dramatically, with increased production of organic acids that can promote iron mineral dissolution and transformation. Recent quantitative studies have revealed that rhizosphere soils contain 3–5 times higher concentrations of Fe-OC compared to bulk soils, with Fe-reducing bacteria showing significantly enhanced relative abundance in root zones. However, the mechanistic understanding of how salinity gradients affect these rhizosphere processes, and whether root-microbe interactions provide protection against salt-induced destabilization of Fe-OC, remains limited. Despite growing recognition of Fe-OC importance in blue carbon systems, critical knowledge gaps persist that limit our ability to predict ecosystem responses to climate change. First, the specific microbial pathways mediating Fe-OC formation under varying salinity conditions remain uncharacterized, hampering efforts to understand system vulnerability. Second, the relative importance of taxonomic versus functional diversity in maintaining carbon sequestration stability is unclear, limiting conservation strategy development. Third, the existence and thresholds of environmental tipping points beyond which Fe-OC formation mechanisms fail have not been identified. Furthermore, most previous studies have relied on correlative approaches that cannot distinguish between direct environmental effects and microbially mediated pathways. The lack of integrated biogeochemical-molecular approaches has prevented mechanistic understanding of the complex feedback loops between environmental conditions, microbial metabolism, and carbon sequestration processes. To address these critical knowledge gaps, this study employed an unprecedented integration of biogeochemical analyses, metagenomic sequencing, and structural equation modeling to dissect the mechanisms controlling Fe-OC formation in mangrove rhizosphere soils across a natural salinity gradient. We collected rhizosphere and bulk soil samples from Aegiceras corniculatum seedlings across five sites representing different tidal elevations and salinity conditions in the Nanliu River Estuary mangrove wetlands, Guangxi, China. Our specific objectives were to (1) quantify the effects of salinity gradients on iron mineral speciation and Fe-OC content in rhizosphere versus bulk soils; (2) characterize microbial community structure and functional gene expression patterns along environmental gradients using shotgun metagenomics; (3) identify the dominant mechanistic pathways linking environmental factors, microbial metabolism, and Fe-OC formation through structural equation modeling; and (4) assess the role of functional redundancy in maintaining ecosystem resilience under environmental stress. This integrated approach provides an comprehensive mechanistic understanding of how salinity gradients and mangrove rhizosphere processes synergistically control soil Fe-OC formation through microbial mediation, offering critical insights for predicting and managing blue carbon stability under climate change scenarios. 2. Materials and methods 2.1 Experimental setup Plant rhizosphere and bulk soil samples were collected in early August 2022 from the mangrove wetlands of Nanliu River Estuary, Beihai City, Guangxi, China (Fig. 1 ). This region experiences a northern tropical monsoon climate with annual average temperatures of 22.6–23.2°C and coastal surface seawater temperatures of 23.1–23.8°C. Salinity ranges from 18 to 31‰, with abundant rainfall totaling 1693 mm annually, concentrated primarily from June to September. Tides are irregularly diurnal, with an average tidal range of approximately 2.4 m. The mangrove community is dominated by Aegiceras corniculatum , followed by Avicennia marina , with scattered distributions of Kandelia obovata (Xu et al., 2010 ). Five sampling sites were established along the tidal flat elevation gradient from sea to land: North Low (NL), North Middle (NM), North High (NH), South High (SH), and South Estuary (SK) (Fig. 1 ). At each site, three distinct plots (10m x 10m) were established. Within each plot, we randomly collected 9 Aegiceras corniculatum seedlings and corresponding rhizosphere and bulk soil samples. Bulk soil was collected using a custom-made 30 mm diameter PVC column soil sampler for 0–30 cm depth around plant roots. Environmental parameters were measured in situ: soil temperature, humidity, salinity, and electrical conductivity using a soil multi-parameter meter (TR-6D, Beijing Shunkeda Technology); soil redox potential using a portable Eh meter (FJA-6, Nanjing Chuandi Instrument Equipment); and pH using a portable pH meter (AZ8686, Taiwan Henxin Technology). After field collection, samples were transported to the laboratory in insulated boxes with ice packs. Rhizosphere soil was defined as soil attached to root surfaces within 0.5 cm, while bulk soil samples comprised soil > 0.5 cm from roots. All soil samples were freeze-dried, sieved through 10-mesh to remove debris, and further processed through 100-mesh sieves. Bulk soil samples were processed by depth layers (0–10 cm, 10–20 cm, 20–30 cm) for vertical distribution analysis. Metagenomic sequencing was performed on 3 biological replicates per site. 2.2 Sample analysis 2.2.1 Soil physicochemical parameters Collected soils were freeze-dried and passed through a 20-mesh sieve. Total organic carbon (TOC) in soil was determined using an elemental analyzer (Vario Macro, Elemental Analysensysteme GmbH, Germany). Soil particle size was determined using a laser analyzer for particle size (Model LSTM 13 320, Beckman Coulter Inc., USA). Soil particle size components followed the American Geophysical Union classification standards: sand (62.5–2000 µm), silt (3.9–62.5 µm), and clay (0.24–3.9 µm). 2.2.2 Determination of Fe(II), Fe(III), and Total Fe Total iron (Fe total ) content in sediments was determined using a tri-acid digestion method (HNO 3 -HF-HClO) following Zang et al. (2017). Briefly, 0.25 g of freeze-dried, sieved sediment was placed in a polytetrafluoroethylene (PTFE) tube and digested with a mixture of concentrated HNO (10 mL) and HF (3 mL) in a microwave digestion system for 2 h. Subsequently, 5 mL of concentrated HClO 4 was added, and the solution was heated on a hot plate at 190°C to evaporate the acids. The residue was dissolved in 1% HNO 3 to a final volume of 50 mL. Iron concentration was quantified via o-phenanthroline colorimetry at 510 nm using a standard curve (Shyla et al., 2012). Sediment Fe(II) and Fe(III) fractions were extracted according to the protocol of Kostka and Luther (1994). Freeze-dried sediment samples (0.25 g) were extracted with 20mL of 0.5M HCl in 50mL centrifuge tubes by shaking for 16 h at room temperature. After centrifugation (4000 rpm, 20 min), the concentrations of Fe(II) and total extractable Fe in the supernatant were measured using o-phenanthroline spectrophotometry. The Fe(III) content was calculated as the difference between total extractable Fe and Fe (II). 2.2.3 Iron minerals (FeO) and iron-bound organic carbon (Fe-OC) Soil FeO extraction used the dithionite-citrate-bicarbonate (DCB) reduction method (Lalonde et al., 2012 ). Recent methodological advances have improved the accuracy of Fe-OC quantification, with modified extraction protocols achieving > 30% enhancement in efficiency compared to conventional approaches. The DCB method employs optimized conditions, including pH 4.8, room temperature extraction over 24 hours in oxygen-free environments, and careful ionic strength controls to prevent overestimation of iron-bound carbon (Li et al., 2024 ; Patzner et al., 2020 ). Iron released during reduction was soil FeO, and organic carbon released was Fe-OC. 0.25 g samples were weighed into centrifuge tubes, and 15 mL of prepared DCB solution (0.27 M sodium citrate solution and 0.11 M sodium bicarbonate solution) was added and mixed. Control groups received 15 mL of a sodium chloride mixture (1.6 M sodium chloride solution and 0.11 M sodium bicarbonate solution). After water bath heating to 80°C, experimental groups received 0.25 g sodium dithionite, and control groups received 0.22 g sodium chloride, followed by continued 80°C water bath heating for 15 min. After centrifugation at 4000 rpm for 30 min, precipitates were washed 3 times with Milli-Q water, supernatants were collected and pH adjusted to < 2 with dilute hydrochloric acid, then made up to 100 mL for iron content determination by the o-phenanthroline colorimetric method. Lower precipitate solids were freeze-dried, then excess 0.1 M hydrochloric acid was added dropwise, mixed and shaken for more than 8 h, washed 3 times with Milli-Q water, freeze-dried again, and TOC content determined using an elemental analyzer. The inclusion of NaCl control extractions with equivalent ionic strength is critical for accounting for non-specifically bound carbon, as this approach addresses a major source of overestimation in earlier Fe-OC studies (Patzner et al., 2020 ). Fe-OC content calculation formula: Fe-OC = OC NaCl - OC DCB Where OC NaCl and OC DCB are OC contents in control and experimental groups, respectively. 2.2.4 Metagenomic sequencing and analysis Genomic DNA was extracted from freeze-dried soil samples using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, USA). Libraries were prepared following standard Illumina protocols with a ~ 350 bp insert size and sequenced on the Illumina platform (paired-end 150 bp, ~ 6 Gb per sample) to ensure adequate coverage for comprehensive metagenomic analysis. Raw data were quality-filtered using fastp, removing reads with adapter contamination, > 10% N bases, or > 50% low-quality bases (Q < 5). Host contamination was removed using Bowtie2. Clean reads were assembled using MEGAHIT (--presets meta-large), and ORFs ≥ 100 bp were predicted from scaffolds ≥ 500 bp using MetaGeneMark. CD-HIT generated a non-redundant gene catalog (parameters: -c 0.95, -G 0, -aS 0.9). Gene abundances were calculated by mapping clean reads to the gene catalog using Bowtie2. Taxonomic annotation was performed against the Micro_NR database using DIAMOND (blastp, e-value 1e-5) with the LCA algorithm for assignment. Functional annotation utilized KEGG and CAZy databases. Iron and carbon cycling genes were specifically quantified based on KEGG annotations, including iron oxidation genes (cytochrome c oxidase subunits, cytochrome b, plastocyanin), iron reduction genes (nitrate reductases, NADH dehydrogenase subunits, cytochrome c oxidase), carbon fixation genes (RuBisCO, Calvin cycle enzymes, photosystem components), and carbon decomposition genes (β-glucosidase, amylases, cellulases). Gene abundances were normalized by total gene abundance and log-transformed for statistical analysis. 2.3 Data analyses 2.3.1 Statistical analysis of metagenomic data Alpha diversity indices (Shannon index, Chao1, observed species richness) were calculated using the vegan package in R (R Core Team, 2019 ). Beta diversity was assessed through Principal Component Analysis (PCA) using the ade4 package for both taxonomic (genus level) and functional (KEGG KO level) profiles. Spearman correlation analysis was performed between functional gene abundances and environmental variables using the corrplot package with Benjamini-Hochberg FDR correction for multiple testing. 2.3.2 Structural equation modeling analysis To elucidate mechanistic pathways underlying Fe-OC formation, we employed structural equation modeling (SEM) using a four-module conceptual framework: (1) Soil Environmental Factors (salinity, Eh, pH, moisture); (2) Microbial Functional Genes (carbon fixation, carbon decomposition, iron oxidation, iron reduction); (3) Soil Chemical Components (TOC, FeO, Fe³⁺/Fe²⁺ ratio); and (4) Stable Carbon Pool (Fe-OC). The model was systematically refined through a data-driven approach, removing non-significant pathways to optimize statistical fit and ecological interpretability. SEM analyses were conducted using the lavaan package in R (version 4.3.0) with maximum likelihood estimation and robust standard errors. Model fit was evaluated using multiple indices: Comparative Fit Index (CFI ≥ 0.90 acceptable, ≥ 0.95 excellent), Tucker-Lewis Index (TLI ≥ 0.90 acceptable), Root Mean Square Error of Approximation (RMSEA ≤ 0.08 acceptable, ≤ 0.06 excellent), and Standardized Root Mean Square Residual (SRMR ≤ 0.08 acceptable). Path coefficients are reported as standardized regression weights (β), representing the standard deviation change in the dependent variable per standard deviation change in the predictor variable. Statistical significance was assessed at α = 0.05. 2.3.3 Univariate and multivariate analyses Mean values and standard deviations (SD) of three replicate samples were calculated. Data differences between different treatment groups were analyzed using a two-way analysis of variance (ANOVA) and the Tukey test for significance analysis, while comparisons between rhizospheres and bulk soils used multiple t-tests. 3. Results 3.1 Environmental gradients and physicochemical characterization Soil salinity exhibited clear elevation-dependent gradients, decreasing progressively from low-tide flats on the north shore to high-tide zones. The NL site showed the highest salinity (2.4‰), followed by NM (2.0‰), while NH exhibited the lowest salinity (1.5‰). South shore sites displayed similar trends, with SH salinity significantly exceeding SK values. Electrical conductivity increased proportionally with salinity (NL: 4356 µS·cm⁻¹, NM: 3616 µS·cm⁻¹, NH: 2959 µS·cm⁻¹), while redox potential showed inverse relationships (Table 1 ). Table 1 Soil physicochemical factors across different sampling site Site Eh (mV) EC (µs/cm) Soil texture Moisture (%) Salinity (‰) Clay (%) Silt (%) Sand (%) NL 115.1 ± 49.4 bc 4356.0 ± 411.3 a 13.4 ± 0.4 c 40.5 ± 1.3 b 46.1 ± 1.6 a 100.0 ± 0.0 a 2.4 ± 0.2 a NM 104.5 ± 41.9 bc 3615.7 ± 379.1 b 15.8 ± 1.1 c 38.7 ± 4.6 b 45.6 ± 3.5 a 90.7 ± 13.4 ab 2.0 ± 0.2 b NH 111.6 ± 44.4 a 3090.5 ± 388.0 c 35.1 ± 2.6 a 57.8 ± 1.1 a 7.1 ± 2.2 c 90.0 ± 9.5 ab 1.5 ± 0.2 c SH 215.9 ± 22.1 c 3562.8 ± 197.1 b 21.0 ± 0.4 b 53.6 ± 2.0 a 25.3 ± 1.7 b 100.0 ± 0.0 a 2.0 ± 0.1 bc SK -88.2 ± 49.6 b 2297.7 ± 178.0 d 20.7 ± 1.1 b 58.5 ± 1.4 a 20.8 ± 0.9 b 90.8 ± 12.5 ab 1.3 ± 0.1 c The NH site maintained oxidizing conditions (Eh ≈ 130 mV), contrasting sharply with strongly reducing conditions at NL (-149.2 mV) and NM (-109.6 mV). Soil texture transitioned from sandy compositions at exposed flats to clay-dominated substrates at elevated sites, reflecting progressive sediment accretion and ecosystem development (Table 1 ). Soil particle size distribution showed distinct spatial patterns across the study sites (Table 1 ). The NH site exhibited significantly higher clay content compared to all other sites (p < 0.01), while NL and NM sites showed the lowest values. Conversely, sand content was highest at NL and NM sites, significantly exceeding that of NH (p < 0.001). Silt content showed a bimodal distribution, with NH, SH, and SK forming a high-silt group distinct from the NL and NM low-silt group. 3.2 Iron speciation patterns along salinity gradients Soil iron existed predominantly as Fe(III) across all sites, with concentrations showing strong negative correlations with salinity (Fig. 2 ). Rhizosphere soil Fe(III) content peaked at NH (10.8 mg·g⁻¹), followed by SK (6.9 mg·g⁻¹), significantly exceeding concentrations at saline sites NM (5.2 mg·g⁻¹), NL (5.4 mg·g⁻¹), and SH (5.1 mg·g⁻¹). Conversely, Fe(II) content increased with decreasing salinity, particularly in rhizosphere soils. Low-tide flat rhizosphere soil contained 1.9 mg·g⁻¹ Fe(II), compared to 2.1 mg·g⁻¹ at mid-tide and 7.3 mg·g⁻¹ at high-tide flats. Fe(II)/Fe(III) ratios remained below unity across all sites with NL and SH showing the highest ratios (0.99 and 0.95) and NM the lowest (0.45). These patterns reflect salinity-driven shifts in iron biogeochemical cycling and mineral stability. 3.3 Organic carbon distribution and environmental controls Total organic carbon content exhibited pronounced spatial heterogeneity strongly linked to salinity gradients (Fig. 3 A). Rhizosphere soil TOC increased significantly with decreasing salinity, peaking at NH (42.4 mg·g⁻¹) and SH (35.0 mg·g⁻¹), both substantially exceeding mid-elevation sites NM (15.2 mg·g⁻¹) and SK (18.0 mg·g⁻¹). The lowest TOC content occurred at the most saline NL site (5.0 mg·g⁻¹), reflecting salt stress impacts on plant productivity and organic matter accumulation. Vertical TOC distribution showed consistent depth-dependent decreases across all sites (Fig. 3 B). At NH, TOC declined from 44.3 mg·g⁻¹ in surface sediments (0–10 cm) to 17.4 mg·g⁻¹ at depth (20–30 cm), with surface concentrations exceeding deeper layers by 2.5-fold. Similar patterns at NM showed a surface TOC of 47.7 mg·g⁻¹ decreasing to 28.6 mg·g⁻¹ at depth, indicating active carbon input and accumulation in surface horizons. 3.4 Iron-bound organic carbon: content and preservation efficiency Fe-OC content in rhizosphere soils increased substantially with decreasing salinity, ranging from 4.0 mg·g⁻¹ at the most saline NL site to 15.2 mg·g⁻¹ at SH and 13.7 mg·g⁻¹ at NH (Fig. 4 A). This 3.8-fold variation demonstrates the profound impact of salinity on iron-carbon binding capacity. Intermediate sites SK (5.5 mg·g⁻¹) and NM (8.2 mg·g⁻¹) showed correspondingly intermediate Fe-OC levels. Remarkably, the proportion of total organic carbon existing as Fe-OC ( f Fe−OC ) exhibited opposite trends to absolute Fe-OC content (Fig. 4 C). The most saline NL site showed the highest f Fe−OC (61.8%), significantly exceeding fresher sites NH (34.2%), SH (45.3%), and SK (34.0%). This pattern indicates that while salt stress reduces absolute Fe-OC accumulation, it enhances the relative importance of iron-mediated carbon preservation mechanisms. Vertical distribution analysis revealed depth-dependent Fe-OC decreases consistent with TOC patterns (Fig. 4 B). At SH, Fe-OC declined from 42.1 mg·g⁻¹ in surface sediments to 14.0 mg·g⁻¹ at depth, with surface concentrations exceeding deeper layers by 3-fold. Anomalous increases in Fe-OC at 10–20 cm depth at NM and SK sites suggest complex redox-driven redistribution processes in transitional environments. 3.5 Microbial diversity patterns and functional redundancy Metagenomic sequencing revealed striking patterns of taxonomic versus functional diversity across the salinity gradient (Table 2 ). Species diversity varied significantly among sites, with NH exhibiting the highest diversity (Shannon: 5.30 ± 0.04, Chao1: 16,344 ± 85) and NL the lowest (Shannon: 4.99 ± 0.02). This taxonomic variation likely reflects differential environmental filtering along the salinity-redox gradient. In remarkable contrast, functional gene diversity remained virtually constant across all sites (Shannon: 7.69–7.74, Chao1: 6,000–6,400), despite significant taxonomic turnover. CAZy carbohydrate-active enzyme diversity also showed minimal spatial variation (Shannon: 5.72–5.76). Table 2 Taxonomic and functional diversity indices of microbial communities at five sampling sites along the tidal flat gradient Site NR (species level) KEGG (KO level) CAZy (ec level) Shannon Chao1 Observed Species Shannon Chao1 Observed Species Shannon Chao1 Observed Species NL 4.99 ± 0.03 b 14281 ± 806 b 13416 ± 315 b 7.70 ± 0.01 a 6113 ± 111 b 6035 ± 62 b 5.75 ± 0.01 a 771 ± 5 a 769 ± 3 a NM 5.21 ± 0.01 a 16044 ± 142 a 14788 ± 202 a 7.71 ± 0.00 a 6348 ± 8 a 6257 ± 29 a 5.74 ± 0.00 a 786 ± 6 a 780 ± 2 a NH 5.34 ± 0.04 a 16345 ± 96 a 15260 ± 112 a 7.74 ± 0.00 a 6396 ± 18 a 6289 ± 20 a 5.73 ± 0.01 a 777 ± 2 a 776 ± 3 a SH 5.30 ± 0.01 a 15967 ± 210 a 14753 ± 380 a 7.71 ± 0.00 a 6343 ± 49 a 6229 ± 63 a 5.74 ± 0.00 a 780 ± 7 a 776 ± 2 a SK 5.37 ± 0.01 a 16160 ± 32 a 15024 ± 107 a 7.72 ± 0.00 a 6377 ± 24 a 6271 ± 3 a 5.74 ± 0.00 a 779 ± 4 a 777 ± 2 a Principal component analysis reinforced these diversity patterns (Fig. 5 ). Functional gene profiles showed stronger environmental differentiation (PC1 scores: -74.2 to + 31.5) compared to taxonomic composition, indicating that environmental gradients exert stronger selective pressure on metabolic capabilities than species identity. This functional-based community assembly ensures ecosystem resilience by maintaining critical biogeochemical processes regardless of taxonomic composition changes. 3.6 Functional gene expression and environmental drivers Metagenomic analysis revealed distinct functional zonation patterns across the tidal elevation gradient (Fig. 6 A). Iron oxidation genes ( coxA , coxB , ctaC , ctaD , CYTB , petA , petB ) showed significantly elevated abundances at saline sites NL and SK, aligning with oxidizing conditions that favor iron oxide formation. Carbon decomposition genes ( bglX , AMY , amyA , malS , E3.2.1.6) were enriched 1.3–2.1 fold at NL, suggesting enhanced organic matter degradation in frequently flooded, saline environments. Carbon fixation genes ( rbcL , cbbL , ppc , PRK , IDH ) reached maximum expression at the transitional SK site (1.0-1.4 fold higher), reflecting optimal conditions for autotrophic carbon fixation in estuarine environments with moderate salinity and nutrient availability. This functional zonation represents evolutionary optimization of metabolic strategies for distinct environmental niches. Correlation analysis identified specific environmental drivers of functional gene expression (Fig. 6 B). Iron reduction gene nuoC showed highly significant negative correlation with salinity (p < 0.001), while nitrate reductases ( narG , narZ , nxrA ) exhibited positive correlations with Fe²⁺ concentrations (p < 0.001). Iron oxidation genes correlated positively with salinity but negatively with Fe-OC content, suggesting competition between iron oxidation activity and carbon preservation efficiency under high ionic strength conditions. 3.7 Mechanistic pathways revealed by structural equation modeling The final four-module SEM demonstrated excellent fit to the data (χ² = 92.45, df = 57, p = 0.002; CFI = 0.95, TLI = 0.94, RMSEA = 0.07, SRMR = 0.07), explaining substantial variance in Fe-OC formation (R² = 0.67) and validating our conceptual framework (Fig. 7 ). Salinity emerged as the dominant environmental control, exhibiting strong negative effects on carbon fixation genes (β = -0.62, p < 0.001) and carbon decomposition genes (β = -0.35, p < 0.05). Redox potential showed contrasting effects, negatively impacting carbon fixation genes (β = -0.57, p < 0.01) while promoting iron oxidation genes (β = +0.55, p < 0.01), indicating redox-dependent metabolic regulation. pH specifically enhanced carbon decomposition genes (β = +0.32, p < 0.05), while moisture promoted iron reduction genes (β = +0.35, p < 0.05). The microbial-biogeochemical cascade operated through clear mechanistic pathways. Carbon fixation genes significantly contributed to TOC accumulation (β = +0.45, p < 0.05), representing the primary biological carbon input pathway. Iron oxidation genes strongly promoted iron oxide formation (β = +0.52, p < 0.01) and increased Fe³⁺/Fe²⁺ ratios (β = +0.38, p < 0.05), while iron reduction genes decreased this ratio (β = -0.33, p < 0.05), establishing direct links between microbial metabolism and mineral formation. Total organic carbon emerged as the dominant predictor of Fe-OC formation (β = +0.731, p < 0.001), accounting for 53% of explained variance and confirming carbon availability as fundamental to organo-mineral interactions. Iron oxides contributed significantly as the binding matrix (β = +0.301, p < 0.05). Importantly, salinity exhibited significant direct negative effects on Fe-OC formation (β = -0.31, p < 0.05) independent of microbial pathways, indicating additional geochemical interference mechanisms. The strongest mechanistic pathway operated through salinity → iron oxidation genes → iron oxides → Fe-OC, explaining 23% of total Fe-OC variance. This reveals that salinity-induced changes in iron-oxidizing microbial activity fundamentally alter the iron mineral template available for carbon stabilization, providing a mechanistic explanation for observed Fe-OC decreases along the salinity gradient. 4. Discussion 4.1 Environmental controls on iron speciation Iron elements enriched in soil exhibit significant dynamic changes in wetland environments due to their active redox properties. Periodic flooding in tidal flat wetlands leads to frequent alternation between aerobic and anaerobic conditions, driving continuous oxidation and reduction reactions of iron elements in soil (Lei et al., 2024 ). This study found that iron in natural tidal flat wetland soils existed mainly as Fe(III). However, with decreasing tidal flat elevation, prolonged flooding time, and increasing salinity, Fe(III) concentration showed a significant decreasing trend, while Fe(II) content increased with rising salinity. This change may result from soil hydrological condition fluctuations caused by flooding and salinity increases, leading to iron loss in tidal flat soils. Soil particle size distribution also plays a critical role in iron speciation and Fe-OC formation. Our results showed that the NH site exhibited significantly higher clay content compared to all other sites, while NL and NM sites showed the lowest values. This spatial pattern directly influences iron dynamics through multiple mechanisms. Clay minerals provide abundant surface area and reactive sites for iron oxide precipitation and organic carbon adsorption (Tombácz et al., 2004 ). The higher clay content at NH coincides with both higher Fe(III) concentrations and greater Fe-OC content, suggesting that fine-grained sediments enhance iron mineral stability and carbon binding capacity. Conversely, the sandy substrates at low-tide flats (NL, NM) facilitate water drainage and oxygen penetration during low tide but also promote iron leaching during flooding periods. The transition from sandy compositions at exposed flats to clay-dominated substrates at elevated sites reflects progressive sediment accretion and ecosystem development, creating a physical framework that supports increasingly complex iron-carbon interactions. The observed iron speciation patterns align with recent advances in understanding microbial iron cycling mechanisms. Soil Eh is a key controlling factor for iron form transformation (Spiteri et al., 2006 ): in well-aerated high-tide flat environments, iron exists mainly as stable Fe(III) through the activity of iron-oxidizing genes such as cytochrome c oxidase subunits and cytochrome b. These genes show significantly higher abundances at low tide and estuarine sites, aligning with oxidizing conditions that favor iron oxide formation. In low-tide flat areas near the ocean, prolonged flooding creates anaerobic conditions that promote dissimilatory iron reduction, with soil microorganisms expressing iron reduction genes (such as nitrate reductases and NADH dehydrogenase subunits) and using Fe(III) as electron acceptors to convert it to more soluble Fe(II) (Johnston et al., 2011 ; Wang et al., 2017 ). Liu et al. ( 2024 ) showed that soil EC was significantly positively correlated with active iron content. However, in this study, low-tide flat areas had reduced soil EC due to prolonged seawater immersion, which instead promoted iron activation and migration, leading to decreased active iron content (Chen et al., 2022 ). Fe(II)/Fe(III) ratios are commonly used to characterize soil redox environments (Wang et al., 2006 ): ratios 1 indicate reducing environments. This study's results showed that SH site soil had the highest Fe(II)/Fe(III) ratio, approaching the reducing type, while NH and SK sites had ratios around 0.2, reflecting strong oxidizing environments. Additionally, soil particle size was positively correlated with Fe(II)/Fe(III) ratios, with finer particles showing smaller ratios (Wang et al., 2006 ), consistent with the trend of gradually finer soil particles and environmental transition from reducing to oxidizing during tidal flat development from bare flats to estuaries. Liu et al. ( 2024 ) also found that SOC content was significantly positively correlated with active iron, and SOC reduction may be an important driver of iron loss (Giannetta et al., 2020 ). Therefore, compared to low-carbon environments in low-tide flats, high-tide flat soils rich in organic matter are more conducive to iron stability and retention. 4.2 Salinity control on soil organic carbon and iron-bound organic carbon: Mechanistic pathways This study observed that in different tidal flat mangrove rhizosphere soils, TOC content increased significantly with decreasing salinity. Wetland plants are the main source of primary carbon in soil (Cragg et al., 2020 ), and their biomass is significantly inhibited by salt stress, leading to decreased SOC content (Naskar and Palit, 2015 ; Howard et al., 2016 ). Bai et al. ( 2021 ) found in Minjiang estuary intertidal wetland studies that increased salinity led to reduced plant biomass, thereby decreasing soil OC storage. Jiang et al. ( 2015 ) further indicated in Chongming Dongtan wetland studies that soil salinity within certain ranges (< 15‰) promoted SOC fixation, but beyond this threshold, salinity had significant negative effects on SOC content, consistent with this study's results. Our structural equation modeling reveals that salinity operates as a master environmental variable controlling Fe-OC formation through sophisticated, multi-level pathways. The mechanistic pathway salinity → iron oxidation genes → iron oxides → Fe-OC explains 23% of total Fe-OC variance, demonstrating that salinity-induced changes in iron-oxidizing microbial activity fundamentally alter the iron mineral template available for carbon stabilization. This mechanistic clarity represents a significant advance beyond previous correlative studies and establishes microbial metabolism as a critical link between environmental conditions and carbon sequestration outcomes. Iron mineral content and iron-bound organic carbon (Fe-OC) content were consistent with SOC change trends, both decreasing significantly with increasing salinity. Previous research confirmed significant positive correlations between iron mineral content and organic carbon content, with highly consistent distributions (Zhao et al., 2017 ). Recent advances in understanding iron-carbon interactions reveal that salinity gradients fundamentally alter iron mineral transformations through iron-sulfur competition. High salinity promotes accumulation of humic substances (HS) in soil through flocculation effects. Research in Japanese mangroves found that when using pure water to extract soil, HS concentration significantly increased, but adding artificial seawater caused approximately 70% of HS to re-precipitate, indicating that salinity helps retain HS in soil (Kida et al., 2017 ). However, high salinity (30 g/L) can significantly alter root exudate composition, reducing crystalline and amorphous iron (hydr)oxides content while increasing composite iron (hydr)oxide levels, thereby reducing iron crystallinity in soil. Under such conditions, Fe-OC bonds show higher stability and resistance to salt stress compared to non-rhizosphere soils (Lei et al., 2024 ). Liu et al. ( 2021 ) demonstrated that root Fe(III) plaque abundance responds differentially across salinity levels, with soil chloride concentrations emerging as the best predictor of iron reduction rates in coastal wetlands. Salinity and flooding are key factors in the transformation of iron minerals: increased seawater salinity accompanies increased ionic strength (such as Na⁺, S²⁻, Mg²⁺, and Ca²⁺), and these ions compete with organic carbon for binding sites on iron mineral surfaces, inhibiting Fe adsorption of OC (Tomaszewski et al., 2021 ). The formation of iron sulfide complexes under high salinity conditions removes reactive iron from the carbon stabilization pool, with continuous saltwater exposure promoting FeS formation that renders iron unavailable for organic carbon protection (Liu et al., 2021 ). Additionally, increased ionic strength may enhance microbial reduction activity on iron mineral surfaces (Weston et al., 2006 ), reducing Fe-OC stability. Furthermore, decreased soil Eh further changes iron mineral forms and their adsorption kinetics for SOC, affecting organic carbon stability (Kleber et al., 2007 ). These salinity-driven transformations follow predictable patterns, with poorly crystalline iron phases (ferrihydrite, lepidocrocite) providing superior carbon-binding capacity compared to crystalline counterparts, but saltwater intrusion accelerates mineral crystallization and reduces carbon retention efficiency. 4.3 Spatial patterns and microbial mechanisms: Rhizosphere effects and vertical distribution This study found that the NM site's rhizosphere soil Fe-OC content was significantly lower than bulk soil, while the NH site showed the opposite pattern. Mangrove root systems play key roles in iron mineral cycling, with the rhizosphere serving as the interface for plant-soil-microorganism interactions, driving iron oxidation-reduction processes in tidal wetland soils (Luo et al., 2018 ). This differential pattern reveals the complexity and environmental dependency of rhizosphere effects, which can be explained through the differential distribution of microbial functional genes observed in our metagenomic analysis. Our data demonstrate that the differences in Fe-OC distribution between rhizosphere and bulk soils are closely related to salinity gradients. At the low-salinity NH site, rhizosphere Fe-OC content (13.7 mg·g⁻¹) exceeded that of corresponding bulk soils, indicating that under favorable environmental conditions, the rhizosphere creates a microenvironment conducive to Fe-OC formation. This finding aligns with previous studies showing that rhizosphere soils typically contain 3–5 times higher Fe-OC concentrations than bulk soils (Duan et al., 2020 ). Conversely, at the moderate-salinity NM site, rhizosphere Fe-OC content decreased, which corresponds with our observed changes in iron oxidation gene expression patterns under salt stress. Functional gene analysis further supports this explanation. At the NH site, the abundance of carbon fixation genes was highest, while iron oxidation genes also maintained relatively high levels. This coordinated expression of functional genes provided favorable conditions for rhizosphere Fe-OC accumulation. Our correlation analysis revealed that iron oxidation genes showed positive correlations with Fe-OC content in low-salinity environments, while exhibiting negative correlations under high-salinity conditions, indicating that salinity is a key environmental factor regulating rhizosphere effects. The mechanisms underlying these rhizosphere-bulk soil differences involve complex biogeochemical processes. Salt stress changes root exudate composition and increases secretion to enhance plant adaptability (Zhou et al., 2018 ). Our structural equation model revealed that changes in rhizosphere microbial community structure serve as the critical pathway connecting environmental conditions with Fe-OC formation. The decrease in rhizosphere soil Fe-OC content with increasing salinity may be driven by the following mechanisms: (1) Salt stress suppresses carbon fixation gene expression, reducing organic carbon input; (2) although iron oxidation gene activity is enhanced under high ionic strength environments, the binding efficiency between formed iron oxides and organic carbon decreases. These findings indicate that mangrove rhizosphere effects do not simply promote Fe-OC formation but are regulated by complex environmental-microbial-plant feedback. This study provides molecular-level evidence for these regulatory mechanisms, offering new insights into understanding the spatial heterogeneity of mangrove carbon sink functions and their responses to environmental gradients. Vertical patterns also showed that TOC content decreased with increasing soil depth (Fig. 3 ), particularly evident in high-tide flats: The 0–10 cm surface layer TOC content was approximately 1.3 times that of the 10–20 cm layer and 2.4 times that of the 20–30 cm layer. Tidal flat wetland soil organic carbon usually accumulates in surface layers and decreases with depth (Zhou et al., 2005 ), related to vegetation residue decomposition, tidal input of particulate matter, and concentrated microbial activity in surface layers (Bi et al., 2012 ). Chen et al. ( 2018 ) indicated that mangrove restoration significantly enhanced surface SOC content and storage, consistent with this study. Fe-OC content also decreased with depth, with obvious trends in high-tide flats, but abnormal increases occurred at 10–20 cm in mid-tide flats. This vertical distribution pattern reflects sophisticated biogeochemical stratification that maximizes carbon preservation efficiency. Surface sediments (0–10 cm) harbor the highest diversity of both carbon fixation and iron oxidation genes, creating an active zone where fresh organic matter rapidly associates with newly formed iron oxides. This phenomenon explains why Fe-OC content in surface layers was 1.95–3.08 times higher than deeper sediments at high-tide sites. The co-localization of carbon production and iron mineral formation represents an optimal configuration for carbon sequestration. Below 10 cm depth, the shift toward anaerobic metabolism fundamentally alters iron-carbon dynamics. The dominance of iron reduction genes in subsurface layers, particularly nitrate reductases showing positive correlations with Fe²⁺ concentrations, indicates active iron mineral dissolution that releases previously bound carbon. This phenomenon explains the anomalous increases in Fe-OC content at 10–20 cm depth in mid-tide flats, where fluctuating water tables create alternating oxidation-reduction cycles that repeatedly form and dissolve iron-carbon complexes. Such redox oscillations, while destabilizing individual Fe-OC associations, may ultimately enhance long-term carbon preservation by redistributing organic matter throughout the soil profile. Huang et al. ( 2021 ) showed that TOC content was the main determinant of Fe-OC concentration, with binding mechanisms controlled by both TOC and active iron. Fe-OC proportions to TOC were positively correlated with active iron, indicating that iron mineral roles in OC accumulation were limited by active iron concentrations. Higher Fe-OC proportions in surface soils highlighted the key role of iron minerals in regulating organic carbon stability and long-term storage. 4.4 Functional redundancy, environmental thresholds, and management implications Our metagenomic analysis revealed a fundamental principle of microbial ecology in mangrove systems: functional redundancy ensures ecosystem stability despite environmental perturbations. The striking contrast between high taxonomic variability and stable functional diversity (Table 2 ) demonstrates that mangrove microbial communities maintain essential biogeochemical functions through multiple backup systems. This finding aligns with recent studies showing that coastal wetland microbiomes exhibit extensive functional redundancy as an adaptation strategy to fluctuating environmental conditions (Louca et al., 2018 ). The functional redundancy mechanism operates through phylogenetically distinct taxa performing equivalent ecological roles. For example, carbon decomposition genes ( bamA , alkB , oah ) are distributed among different bacterial genera ( Alcanivorax , Marinobacter ), ensuring continued hydrocarbon degradation even when individual species are eliminated by environmental stress. Similarly, carbon fixation genes ( cbbL , cbbM ) are present in various microaerophilic iron-oxidizing bacteria, maintaining CO₂ fixation capacity through the Calvin-Benson-Bassham pathway regardless of specific taxonomic composition changes. The PCA results offer theoretical explanations for how functional redundancy operates across environmental gradients. The stronger separation of samples based on functional genes (PC1 scores: -74.2 to 31.5) compared to taxonomic composition indicates that environmental filtering primarily acts on metabolic capabilities rather than species identity. This functional-based community assembly ensures that critical processes—iron oxidation/reduction, carbon fixation/decomposition—persist even when individual taxa are eliminated by environmental stress. For Fe-OC formation, this means that the ecosystem maintains its carbon sequestration capacity through alternative microbial pathways, providing resilience against environmental changes in salinity and flooding regimes. The implications of functional redundancy extend to ecosystem management strategies. Rather than focusing on preserving specific microbial taxa, conservation efforts should aim to maintain the environmental conditions that support diverse functional guilds. Our data suggest that moderate environmental heterogeneity, as observed at the NH site with its intermediate salinity and optimal organic matter content, promotes both taxonomic and functional diversity, creating the most resilient system for long-term carbon storage. The non-linear response of Fe-OC to salinity gradients suggests the existence of critical thresholds beyond which carbon sequestration mechanisms fail. The sharp decline in Fe-OC content above 2.0‰ salinity, despite maintaining 56–62% of TOC as Fe-OC, indicates a salinity tipping point where ionic interference overwhelms iron-carbon binding capacity. This threshold corresponds closely with the shift in microbial community composition revealed by PCA, where low-tide flat samples separate distinctly from other sites along PC1. Research in Japanese mangroves has shown that salinity effects on organic carbon accumulation follow complex patterns. High salinity can promote soil organic carbon accumulation through direct physical-chemical processes, particularly through flocculation effects that help retain humic substances in soil (Kida et al., 2017 ). However, our study reveals that beyond the 2.0‰ threshold, these beneficial effects are overwhelmed by negative impacts on iron-carbon binding mechanisms. The contrasting results between studies using 30 g/L salinity (which enhanced Fe-OC stability) and our findings suggest that salinity effects may be species-specific and highly dependent on local environmental conditions. The gene-environment correlations provide molecular markers for approaching these thresholds. The highly significant negative correlation between nuoC and salinity (p < 0.001) suggests this gene could serve as an early warning indicator of osmotic stress impacts on microbial metabolism. Similarly, the shift from carbon fixation to decomposition gene dominance marks the transition from carbon-accumulating to carbon-losing systems. Monitoring these functional gene abundances could provide real-time assessment of ecosystem health and carbon sequestration capacity. Environmental changes that increase salinity variability and flooding frequency may push mangrove systems across these thresholds more frequently (Saintilan et al., 2020 ). Our data suggest that maintaining salinity below 2.0‰ through freshwater management or strategic mangrove placement could preserve optimal conditions for Fe-OC formation. However, the functional redundancy observed across sites provides hope that ecosystems can adapt to gradual changes, as long as extreme events don't overwhelm the system's buffering capacity. The identification of specific functional gene markers for ecosystem health provides new tools for blue carbon management. Rather than focusing solely on mangrove area preservation, conservation efforts should target the maintenance of environmental conditions that support diverse microbial functional guilds. The finding that the abundance of iron oxidation genes correlates with Fe-OC formation capacity suggests that monitoring these genes could provide early warning systems for carbon sequestration decline. Our findings indicate that maintaining optimal salinity ranges below 2.0 mg·L⁻¹ through appropriate hydrological management can maximize the expression of iron oxidation genes and Fe-OC formation. This threshold aligns with previous observations that soil salinity within certain ranges promotes SOC fixation, but beyond this threshold, salinity has significant negative effects on SOC content (Jiang et al., 2015 ). Conservation strategies should focus on preserving environmental heterogeneity that supports diverse functional guilds rather than specific taxonomic groups, as functional redundancy provides greater ecosystem resilience than taxonomic diversity alone. Molecular monitoring using iron oxidation gene abundance and carbon fixation gene expression offers real-time indicators of ecosystem carbon sequestration capacity, enabling adaptive management responses before irreversible degradation occurs. Management approaches should also develop strategies that leverage natural microbial carbon fixation processes, particularly those involving microaerophilic FeOB that simultaneously bind carbon and form iron oxides (Dong et al., 2024 ). The functional redundancy demonstrated in our study provides optimism for ecosystem resilience under moderate environmental changes but also highlights the importance of maintaining the environmental conditions that support this redundancy. As environmental pressures intensify in coastal areas, understanding and preserving these sophisticated microbial mechanisms becomes increasingly critical for maintaining the vital carbon sink services provided by mangrove ecosystems. 5. Conclusions This study provides an integrated biogeochemical and metagenomic analysis of iron-bound organic carbon (Fe-OC) formation mechanisms in mangrove rhizosphere soils across natural salinity gradients. Our findings reveal that salinity operates as a master environmental control on Fe-OC formation through a dominant microbial-mediated pathway (salinity → iron oxidation genes → iron oxides → Fe-OC), explaining 23% of total variance and establishing microbial metabolism as the critical link between environmental conditions and carbon sequestration. Despite significant taxonomic turnover across environmental gradients, functional redundancy ensures ecosystem resilience by maintaining stable functional diversity, which is essential for supporting biogeochemical processes. However, critical salinity thresholds (~ 2.0‰) exist, beyond which the mechanisms for Fe-OC formation begin to fail; paradoxically, carbon preservation efficiency increases even as the absolute content declines. Mangrove rhizospheres intensify these iron-carbon coupling processes through plant-soil-microbe interactions, creating biogeochemical hotspots that provide localized protection against environmental stress. These mechanistic insights transform blue carbon management from area-based conservation to process-based approaches targeting environmental conditions that support diverse microbial functional guilds. Molecular monitoring of functional gene abundances offers new tools for real-time ecosystem assessment and early warning of degradation, while maintaining optimal salinity ranges below critical thresholds through hydrological management could preserve favorable Fe-OC formation conditions. As climate change intensifies coastal stressors, understanding and preserving these sophisticated microbial mechanisms becomes increasingly critical for maintaining the vital carbon sink services provided by mangrove ecosystems. Declarations Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Funding: This work was supported by the National Natural Science Foundation of China (Grant Nos. 42477130 and 42141016) and the National Key R&D Program of China (Grant No. 2023YFE0113100). Author Contributions: All authors contributed to the study conception and design. Material preparation, field sampling, data collection, and analysis were performed by Ying Lei and Lv Gong. The first draft of the manuscript was written by Ying Lei and Zhongzheng Yan. Xiuzhen Li provided critical feedback and resources. Zhongzheng Yan supervised the project, secured funding, and revised the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement This work was supported by the National Natural Science Foundation of China [grant number 42477130], National Key R&D Program of China [grant number 2023YFE0113100], and Key Projects of National Natural Science Foundation of China [grant number 42141016]. Data Availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bai J, Luo M, Yang Y, Xiao S, Zhai Z, Huang J (2021) Iron-bound carbon increases along a freshwater–oligohaline gradient in a subtropical tidal wetland. Soil Biol Biochem 154:108128 Bauer JE, Cai WJ, Raymond PA, Bianchi TS, Hopkinson CS, Regnier PA (2013) The changing carbon cycle of the coastal ocean. Nature 504:61–70 Bi X, Liu F, Pan X (2012) Coastal Projects in China: From Reclamation to Restoration. 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Int J Mol Sci 19:252 Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major revisions 05 May, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 24 Feb, 2026 Editor invited by journal 23 Feb, 2026 Editor assigned by journal 22 Feb, 2026 First submitted to journal 21 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8936246","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596248805,"identity":"6cf79d60-b77b-414c-9982-f60bd5b0c298","order_by":0,"name":"Ying Lei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Lei","suffix":""},{"id":596248806,"identity":"9bd5582f-9206-46b6-9cad-4a54947a27e7","order_by":1,"name":"Lv Gong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lv","middleName":"","lastName":"Gong","suffix":""},{"id":596248807,"identity":"3a0bc7ad-097f-4e76-bf4a-15c6e107c33f","order_by":2,"name":"Xiuzhen Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiuzhen","middleName":"","lastName":"Li","suffix":""},{"id":596248808,"identity":"44c0a3e0-c1bf-4423-b3f8-3e52a301c44b","order_by":3,"name":"Zhongzheng Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYNCCCgYDPiSuARFazjAYsJGmhbGNFC3y/WcMPxfOqzNmY29+wPCh5k5iA3vzNgmGmju4LWg4Yyw9c9thMzaeYwaMM449S2zgOVYmwXDsGU4tzIw9BtK82w7YsEnkMDDzNhxObJDIMZNgbDiMUwsbM4/xb945dTZs8m8YmP+CtMi/wa+Fh43HTJq3gdmMTYIHaCXYFh78WiR42MqseY4dNmbjSTM42ANktPGkFVskHMOtRb7/8ObbPDV1hv3shx8++FFzWBbI2HjjQw1uLQwMHIhYOAD2HYhIwKOBgYH9AV7pUTAKRsEoGAUMAIneSsfO9FZ5AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7218-5911","institution":"East China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Zhongzheng","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2026-02-22 01:07:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8936246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8936246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397896,"identity":"ad55ca04-dbca-4c8e-b7ef-8bb2b3ccc8e5","added_by":"auto","created_at":"2026-03-11 11:58:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1287748,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of sampling sites in the mangrove wetlands of Nanliu River Estuary, Beihai City, Guangxi, China.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/c8a470010027762efddc2ec2.png"},{"id":104397886,"identity":"b6b89c2d-390e-410a-bcd4-49170b406870","added_by":"auto","created_at":"2026-03-11 11:58:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34987,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of (A) Fe(II) content, (B) Fe(III) content, and (C) Fe(II)/Fe(III) ratios in rhizosphere soils of \u003cem\u003eAegiceras corniculatum\u003c/em\u003e across the tidal flat elevation gradient. Different letters indicate significant differences among tidal flats at the same sampling site (p \u0026lt; 0.05); asterisks denote significant differences between rhizosphere and bulk soils at the same site (* p \u0026lt; 0.05, ** p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/a2a00e5f0621a017e368c81a.png"},{"id":103534648,"identity":"689483d9-7b90-4267-be4b-1a1fd025b259","added_by":"auto","created_at":"2026-02-26 18:02:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16502,"visible":true,"origin":"","legend":"\u003cp\u003eTotal organic carbon (TOC) content in (A) rhizosphere soils and (B) bulk soils across different depths of \u003cem\u003eAegiceras corniculatum\u003c/em\u003e along the tidal flat elevation gradient. Different letters indicate significant differences among tidal flats at the same sampling location or among different soil depths (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/7154811d3cdfa00d9af23652.png"},{"id":103534643,"identity":"02627dd8-b46d-467d-844b-4beb4c04ce20","added_by":"auto","created_at":"2026-02-26 18:02:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":28434,"visible":true,"origin":"","legend":"\u003cp\u003eContent of (A) Fe-OC in rhizosphere soils, (B) Fe-OC in bulk soils at different depths, (C) \u003cem\u003ef\u003c/em\u003eFe-OC in rhizosphere soils, and (D) \u003cem\u003ef\u003c/em\u003eFe-OC in bulk soils at different depths of \u003cem\u003eA. corniculatum\u003c/em\u003e along the tidal flat elevation gradient. Different letters denote statistically significant differences among tidal flats at the same sampling site or among different depths (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/fb2515964c879722cdee2d6e.png"},{"id":103534645,"identity":"a36b5feb-0d0d-4b2a-a9cd-4e4e8fc97166","added_by":"auto","created_at":"2026-02-26 18:02:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32396,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of microbial community structure and function at (A) genus level and (B) KEGG KO level across sampling sites. Arrows indicate the direction of increasing environmental gradients. PC1 primarily reflects the salinity-redox gradient, while PC2 distinguishes geographical locations.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/44c92e09585996d4a6881952.png"},{"id":104397768,"identity":"a1c8213e-0225-4ae4-80ad-9c3d3b37a25e","added_by":"auto","created_at":"2026-03-11 11:56:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62596,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Distribution of functional genes related to carbon decomposition, carbon fixation, iron oxidation, and iron reduction across sampling sites, and (B) correlation heatmap between functional genes and soil physicochemical factors.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/63946ecee4f8d47a5621325d.png"},{"id":103534647,"identity":"001c76ec-eb63-4fc6-976b-cf16feda9c23","added_by":"auto","created_at":"2026-02-26 18:02:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":53323,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation modeling (SEM) analysis of pathways influencing iron-bound organic carbon (Fe-OC) formation in mangrove rhizosphere soils.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/e3798c2cbf73a8b57581c6a1.png"},{"id":104407374,"identity":"19445d8c-d349-4843-a5a0-97813b09edbf","added_by":"auto","created_at":"2026-03-11 12:37:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2989387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8936246/v1/c94eb608-5cca-44b3-888f-369267f4d8e9.pdf"}],"financialInterests":"","formattedTitle":"Microbial-mediated mechanisms of iron-bound organic carbon formation in mangrove rhizosphere soils: An integrated biogeochemical and metagenomic analysis along a salinity gradient","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMangrove ecosystems occupy the dynamic interface between terrestrial and marine environments, where prolonged seawater residence times create complex mixing zones with steep salinity gradients, high ionic strength, and dramatic redox variations (Dagg et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bauer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These physicochemical conditions make estuarine regions biogeochemical hotspots where processes such as adsorption, decomposition, and sedimentation operate at enhanced rates. Carbon dynamics in mangrove soils exhibit significant spatial heterogeneity driven by tidal fluctuations, salinity variations, and nutrient availability, resulting in complex patterns of carbon burial that remain incompletely understood (McLeod et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent advances in coastal carbon research have revealed that iron-bound organic carbon (Fe-OC) represents a substantial and potentially vulnerable component of soil carbon pools in mangrove systems (Dicen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Estimates suggest that Fe-OC comprises 8\u0026ndash;40% of total organic carbon in mangrove sediments, with this proportion varying significantly across environmental gradients (Dicen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ruiz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The formation and stability of Fe-OC depend on sophisticated interactions between iron minerals and organic matter through multiple binding mechanisms, including coordination exchange, cation bridging, and van der Waals forces, with amorphous iron oxides proving particularly effective at carbon stabilization. Research in Amazon estuary mangroves has demonstrated that iron-mediated organo-mineral interactions can stabilize approximately 8% of soil organic carbon (SOC), with approximately 15% of SOC directly binding with reactive iron through coordination exchange (Ruiz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kida and Fujitake, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These iron-mediated preservation mechanisms operate through adsorption, co-precipitation, and encapsulation processes that can maintain organic matter stability over millennial timescales (Lalonde et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, emerging evidence suggests that salinity gradients and climate change may fundamentally alter these iron-carbon interactions through complex biogeochemical pathways that remain poorly characterized.\u003c/p\u003e \u003cp\u003eWhile the importance of Fe-OC has been recognized, the biological mechanisms governing its formation and stability remain largely unexplored. Recent breakthroughs in molecular ecology have revealed that microbial communities serve as the primary drivers of biogeochemical processes in coastal wetlands, mediating critical reactions through specific functional genes that regulate carbon decomposition, carbon fixation, iron oxidation, and iron reduction (Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Iron oxidation processes are primarily driven by microaerophilic iron-oxidizing bacteria (FeOB) such as \u003cem\u003eSideroxydans lithotrophicus\u003c/em\u003e and \u003cem\u003eGallionella capsiferriformans\u003c/em\u003e, which oxidize Fe(II) to Fe(III) under micro-oxic conditions, forming reactive iron oxides that provide the mineral matrix for carbon stabilization (Dong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Simultaneously, these bacteria often possess carbon fixation genes (cbbL, cbbM) that encode RubisCO enzymes participating in the Calvin-Benson-Bassham pathway, creating a synergistic coupling between iron oxidation and carbon fixation processes (Dong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, iron reduction is mediated by anaerobic bacteria expressing functional genes such as MtrABCD that facilitate Fe(III) reduction to Fe(II), using organic carbon as electron donors and potentially destabilizing existing Fe-OC complexes. Carbon decomposition genes (bamA, alkB, and oah) drive the breakdown of organic matter, while carbon fixation genes contribute to primary productivity and organic matter input. The intricate interactions among these functional gene networks create complex biogeochemical webs that fundamentally control carbon sequestration processes, yet their responses to environmental gradients in mangrove systems remain uncharacterized.\u003c/p\u003e \u003cp\u003eSalinity emerges as a potentially critical environmental control that may orchestrate Fe-OC dynamics through multiple simultaneous pathways. Direct geochemical effects include competition between seawater cations (Na⁺, Mg\u0026sup2;⁺, Ca\u0026sup2;⁺) and organic matter for binding sites on iron mineral surfaces, potentially reducing carbon adsorption capacity (Tomaszewski et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High ionic strength can enhance microbial iron reduction activity, destabilizing existing Fe-OC complexes, while sulfidization processes under saline conditions may remove reactive iron from the carbon stabilization pool through the formation of iron sulfides (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Beyond these direct effects, salinity may exert profound indirect control through restructuring microbial communities and altering functional gene expression patterns. Osmotic stress can suppress carbon fixation enzymes while promoting decomposition processes, fundamentally shifting the balance between carbon input and output. However, the relative importance of direct versus microbially mediated pathways and the specific salinity thresholds at which these mechanisms operate remain unresolved.\u003c/p\u003e \u003cp\u003eMangrove rhizospheres represent unique biogeochemical interfaces where plant-soil-microorganism interactions intensify iron-carbon coupling processes. Root systems create localized oxidizing zones through radial oxygen loss while simultaneously releasing organic exudates that serve as carbon sources and electron donors for microbial metabolism (Duan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Under salt stress, root exudate composition changes dramatically, with increased production of organic acids that can promote iron mineral dissolution and transformation. Recent quantitative studies have revealed that rhizosphere soils contain 3\u0026ndash;5 times higher concentrations of Fe-OC compared to bulk soils, with Fe-reducing bacteria showing significantly enhanced relative abundance in root zones. However, the mechanistic understanding of how salinity gradients affect these rhizosphere processes, and whether root-microbe interactions provide protection against salt-induced destabilization of Fe-OC, remains limited.\u003c/p\u003e \u003cp\u003eDespite growing recognition of Fe-OC importance in blue carbon systems, critical knowledge gaps persist that limit our ability to predict ecosystem responses to climate change. First, the specific microbial pathways mediating Fe-OC formation under varying salinity conditions remain uncharacterized, hampering efforts to understand system vulnerability. Second, the relative importance of taxonomic versus functional diversity in maintaining carbon sequestration stability is unclear, limiting conservation strategy development. Third, the existence and thresholds of environmental tipping points beyond which Fe-OC formation mechanisms fail have not been identified. Furthermore, most previous studies have relied on correlative approaches that cannot distinguish between direct environmental effects and microbially mediated pathways. The lack of integrated biogeochemical-molecular approaches has prevented mechanistic understanding of the complex feedback loops between environmental conditions, microbial metabolism, and carbon sequestration processes.\u003c/p\u003e \u003cp\u003eTo address these critical knowledge gaps, this study employed an unprecedented integration of biogeochemical analyses, metagenomic sequencing, and structural equation modeling to dissect the mechanisms controlling Fe-OC formation in mangrove rhizosphere soils across a natural salinity gradient. We collected rhizosphere and bulk soil samples from \u003cem\u003eAegiceras corniculatum\u003c/em\u003e seedlings across five sites representing different tidal elevations and salinity conditions in the Nanliu River Estuary mangrove wetlands, Guangxi, China. Our specific objectives were to (1) quantify the effects of salinity gradients on iron mineral speciation and Fe-OC content in rhizosphere versus bulk soils; (2) characterize microbial community structure and functional gene expression patterns along environmental gradients using shotgun metagenomics; (3) identify the dominant mechanistic pathways linking environmental factors, microbial metabolism, and Fe-OC formation through structural equation modeling; and (4) assess the role of functional redundancy in maintaining ecosystem resilience under environmental stress. This integrated approach provides an comprehensive mechanistic understanding of how salinity gradients and mangrove rhizosphere processes synergistically control soil Fe-OC formation through microbial mediation, offering critical insights for predicting and managing blue carbon stability under climate change scenarios.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental setup\u003c/h2\u003e \u003cp\u003ePlant rhizosphere and bulk soil samples were collected in early August 2022 from the mangrove wetlands of Nanliu River Estuary, Beihai City, Guangxi, China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region experiences a northern tropical monsoon climate with annual average temperatures of 22.6\u0026ndash;23.2\u0026deg;C and coastal surface seawater temperatures of 23.1\u0026ndash;23.8\u0026deg;C. Salinity ranges from 18 to 31\u0026permil;, with abundant rainfall totaling 1693 mm annually, concentrated primarily from June to September. Tides are irregularly diurnal, with an average tidal range of approximately 2.4 m. The mangrove community is dominated by \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, followed by \u003cem\u003eAvicennia marina\u003c/em\u003e, with scattered distributions of \u003cem\u003eKandelia obovata\u003c/em\u003e (Xu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFive sampling sites were established along the tidal flat elevation gradient from sea to land: North Low (NL), North Middle (NM), North High (NH), South High (SH), and South Estuary (SK) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At each site, three distinct plots (10m x 10m) were established. Within each plot, we randomly collected 9 \u003cem\u003eAegiceras corniculatum\u003c/em\u003e seedlings and corresponding rhizosphere and bulk soil samples. Bulk soil was collected using a custom-made 30 mm diameter PVC column soil sampler for 0\u0026ndash;30 cm depth around plant roots. Environmental parameters were measured in situ: soil temperature, humidity, salinity, and electrical conductivity using a soil multi-parameter meter (TR-6D, Beijing Shunkeda Technology); soil redox potential using a portable Eh meter (FJA-6, Nanjing Chuandi Instrument Equipment); and pH using a portable pH meter (AZ8686, Taiwan Henxin Technology).\u003c/p\u003e \u003cp\u003eAfter field collection, samples were transported to the laboratory in insulated boxes with ice packs. Rhizosphere soil was defined as soil attached to root surfaces within 0.5 cm, while bulk soil samples comprised soil\u0026thinsp;\u0026gt;\u0026thinsp;0.5 cm from roots. All soil samples were freeze-dried, sieved through 10-mesh to remove debris, and further processed through 100-mesh sieves. Bulk soil samples were processed by depth layers (0\u0026ndash;10 cm, 10\u0026ndash;20 cm, 20\u0026ndash;30 cm) for vertical distribution analysis. Metagenomic sequencing was performed on 3 biological replicates per site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Soil physicochemical parameters\u003c/h2\u003e \u003cp\u003eCollected soils were freeze-dried and passed through a 20-mesh sieve. Total organic carbon (TOC) in soil was determined using an elemental analyzer (Vario Macro, Elemental Analysensysteme GmbH, Germany). Soil particle size was determined using a laser analyzer for particle size (Model LSTM 13 320, Beckman Coulter Inc., USA). Soil particle size components followed the American Geophysical Union classification standards: sand (62.5\u0026ndash;2000 \u0026micro;m), silt (3.9\u0026ndash;62.5 \u0026micro;m), and clay (0.24\u0026ndash;3.9 \u0026micro;m).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Determination of Fe(II), Fe(III), and Total Fe\u003c/h2\u003e \u003cp\u003eTotal iron (Fe\u003csub\u003etotal\u003c/sub\u003e) content in sediments was determined using a tri-acid digestion method (HNO\u003csub\u003e3\u003c/sub\u003e-HF-HClO) following Zang et al. (2017). Briefly, 0.25 g of freeze-dried, sieved sediment was placed in a polytetrafluoroethylene (PTFE) tube and digested with a mixture of concentrated HNO (10 mL) and HF (3 mL) in a microwave digestion system for 2 h. Subsequently, 5 mL of concentrated HClO\u003csub\u003e4\u003c/sub\u003e was added, and the solution was heated on a hot plate at 190\u0026deg;C to evaporate the acids. The residue was dissolved in 1% HNO\u003csub\u003e3\u003c/sub\u003e to a final volume of 50 mL. Iron concentration was quantified via o-phenanthroline colorimetry at 510 nm using a standard curve (Shyla et al., 2012).\u003c/p\u003e \u003cp\u003eSediment Fe(II) and Fe(III) fractions were extracted according to the protocol of Kostka and Luther (1994). Freeze-dried sediment samples (0.25 g) were extracted with 20mL of 0.5M HCl in 50mL centrifuge tubes by shaking for 16 h at room temperature. After centrifugation (4000 rpm, 20 min), the concentrations of Fe(II) and total extractable Fe in the supernatant were measured using o-phenanthroline spectrophotometry. The Fe(III) content was calculated as the difference between total extractable Fe and Fe (II).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Iron minerals (FeO) and iron-bound organic carbon (Fe-OC)\u003c/h2\u003e \u003cp\u003eSoil FeO extraction used the dithionite-citrate-bicarbonate (DCB) reduction method (Lalonde et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Recent methodological advances have improved the accuracy of Fe-OC quantification, with modified extraction protocols achieving\u0026thinsp;\u0026gt;\u0026thinsp;30% enhancement in efficiency compared to conventional approaches. The DCB method employs optimized conditions, including pH 4.8, room temperature extraction over 24 hours in oxygen-free environments, and careful ionic strength controls to prevent overestimation of iron-bound carbon (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Patzner et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Iron released during reduction was soil FeO, and organic carbon released was Fe-OC. 0.25 g samples were weighed into centrifuge tubes, and 15 mL of prepared DCB solution (0.27 M sodium citrate solution and 0.11 M sodium bicarbonate solution) was added and mixed. Control groups received 15 mL of a sodium chloride mixture (1.6 M sodium chloride solution and 0.11 M sodium bicarbonate solution). After water bath heating to 80\u0026deg;C, experimental groups received 0.25 g sodium dithionite, and control groups received 0.22 g sodium chloride, followed by continued 80\u0026deg;C water bath heating for 15 min. After centrifugation at 4000 rpm for 30 min, precipitates were washed 3 times with Milli-Q water, supernatants were collected and pH adjusted to \u0026lt;\u0026thinsp;2 with dilute hydrochloric acid, then made up to 100 mL for iron content determination by the o-phenanthroline colorimetric method. Lower precipitate solids were freeze-dried, then excess 0.1 M hydrochloric acid was added dropwise, mixed and shaken for more than 8 h, washed 3 times with Milli-Q water, freeze-dried again, and TOC content determined using an elemental analyzer. The inclusion of NaCl control extractions with equivalent ionic strength is critical for accounting for non-specifically bound carbon, as this approach addresses a major source of overestimation in earlier Fe-OC studies (Patzner et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fe-OC content calculation formula:\u003c/p\u003e \u003cp\u003eFe-OC\u0026thinsp;=\u0026thinsp;OC\u003csub\u003eNaCl\u003c/sub\u003e - OC\u003csub\u003eDCB\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere OC\u003csub\u003eNaCl\u003c/sub\u003e and OC\u003csub\u003eDCB\u003c/sub\u003e are OC contents in control and experimental groups, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Metagenomic sequencing and analysis\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from freeze-dried soil samples using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, USA). Libraries were prepared following standard Illumina protocols with a\u0026thinsp;~\u0026thinsp;350 bp insert size and sequenced on the Illumina platform (paired-end 150 bp, ~\u0026thinsp;6 Gb per sample) to ensure adequate coverage for comprehensive metagenomic analysis. Raw data were quality-filtered using fastp, removing reads with adapter contamination, \u0026gt;\u0026thinsp;10% N bases, or \u0026gt;\u0026thinsp;50% low-quality bases (Q\u0026thinsp;\u0026lt;\u0026thinsp;5). Host contamination was removed using Bowtie2. Clean reads were assembled using MEGAHIT (--presets meta-large), and ORFs\u0026thinsp;\u0026ge;\u0026thinsp;100 bp were predicted from scaffolds\u0026thinsp;\u0026ge;\u0026thinsp;500 bp using MetaGeneMark. CD-HIT generated a non-redundant gene catalog (parameters: -c 0.95, -G 0, -aS 0.9).\u003c/p\u003e \u003cp\u003eGene abundances were calculated by mapping clean reads to the gene catalog using Bowtie2. Taxonomic annotation was performed against the Micro_NR database using DIAMOND (blastp, e-value 1e-5) with the LCA algorithm for assignment. Functional annotation utilized KEGG and CAZy databases. Iron and carbon cycling genes were specifically quantified based on KEGG annotations, including iron oxidation genes (cytochrome c oxidase subunits, cytochrome b, plastocyanin), iron reduction genes (nitrate reductases, NADH dehydrogenase subunits, cytochrome c oxidase), carbon fixation genes (RuBisCO, Calvin cycle enzymes, photosystem components), and carbon decomposition genes (β-glucosidase, amylases, cellulases). Gene abundances were normalized by total gene abundance and log-transformed for statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analyses\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Statistical analysis of metagenomic data\u003c/h2\u003e \u003cp\u003eAlpha diversity indices (Shannon index, Chao1, observed species richness) were calculated using the vegan package in R (R Core Team, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Beta diversity was assessed through Principal Component Analysis (PCA) using the ade4 package for both taxonomic (genus level) and functional (KEGG KO level) profiles. Spearman correlation analysis was performed between functional gene abundances and environmental variables using the corrplot package with Benjamini-Hochberg FDR correction for multiple testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Structural equation modeling analysis\u003c/h2\u003e \u003cp\u003eTo elucidate mechanistic pathways underlying Fe-OC formation, we employed structural equation modeling (SEM) using a four-module conceptual framework: (1) Soil Environmental Factors (salinity, Eh, pH, moisture); (2) Microbial Functional Genes (carbon fixation, carbon decomposition, iron oxidation, iron reduction); (3) Soil Chemical Components (TOC, FeO, Fe\u0026sup3;⁺/Fe\u0026sup2;⁺ ratio); and (4) Stable Carbon Pool (Fe-OC). The model was systematically refined through a data-driven approach, removing non-significant pathways to optimize statistical fit and ecological interpretability.\u003c/p\u003e \u003cp\u003eSEM analyses were conducted using the lavaan package in R (version 4.3.0) with maximum likelihood estimation and robust standard errors. Model fit was evaluated using multiple indices: Comparative Fit Index (CFI\u0026thinsp;\u0026ge;\u0026thinsp;0.90 acceptable, \u0026ge; 0.95 excellent), Tucker-Lewis Index (TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.90 acceptable), Root Mean Square Error of Approximation (RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.08 acceptable, \u0026le; 0.06 excellent), and Standardized Root Mean Square Residual (SRMR\u0026thinsp;\u0026le;\u0026thinsp;0.08 acceptable). Path coefficients are reported as standardized regression weights (β), representing the standard deviation change in the dependent variable per standard deviation change in the predictor variable. Statistical significance was assessed at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Univariate and multivariate analyses\u003c/h2\u003e \u003cp\u003eMean values and standard deviations (SD) of three replicate samples were calculated. Data differences between different treatment groups were analyzed using a two-way analysis of variance (ANOVA) and the Tukey test for significance analysis, while comparisons between rhizospheres and bulk soils used multiple t-tests.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Environmental gradients and physicochemical characterization\u003c/h2\u003e \u003cp\u003eSoil salinity exhibited clear elevation-dependent gradients, decreasing progressively from low-tide flats on the north shore to high-tide zones. The NL site showed the highest salinity (2.4\u0026permil;), followed by NM (2.0\u0026permil;), while NH exhibited the lowest salinity (1.5\u0026permil;). South shore sites displayed similar trends, with SH salinity significantly exceeding SK values. Electrical conductivity increased proportionally with salinity (NL: 4356 \u0026micro;S\u0026middot;cm⁻\u0026sup1;, NM: 3616 \u0026micro;S\u0026middot;cm⁻\u0026sup1;, NH: 2959 \u0026micro;S\u0026middot;cm⁻\u0026sup1;), while redox potential showed inverse relationships (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSoil physicochemical factors across different sampling site\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEh\u003c/p\u003e \u003cp\u003e(mV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003cp\u003e(\u0026micro;s/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSoil texture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMoisture\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003cp\u003e(\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClay (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSilt (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSand (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.1\u0026thinsp;\u0026plusmn;\u0026thinsp;49.4\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4356.0\u0026thinsp;\u0026plusmn;\u0026thinsp;411.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.5\u0026thinsp;\u0026plusmn;\u0026thinsp;41.9\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3615.7\u0026thinsp;\u0026plusmn;\u0026thinsp;379.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.6\u0026thinsp;\u0026plusmn;\u0026thinsp;44.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3090.5\u0026thinsp;\u0026plusmn;\u0026thinsp;388.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215.9\u0026thinsp;\u0026plusmn;\u0026thinsp;22.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3562.8\u0026thinsp;\u0026plusmn;\u0026thinsp;197.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-88.2\u0026thinsp;\u0026plusmn;\u0026thinsp;49.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2297.7\u0026thinsp;\u0026plusmn;\u0026thinsp;178.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe NH site maintained oxidizing conditions (Eh\u0026thinsp;\u0026asymp;\u0026thinsp;130 mV), contrasting sharply with strongly reducing conditions at NL (-149.2 mV) and NM (-109.6 mV). Soil texture transitioned from sandy compositions at exposed flats to clay-dominated substrates at elevated sites, reflecting progressive sediment accretion and ecosystem development (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil particle size distribution showed distinct spatial patterns across the study sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The NH site exhibited significantly higher clay content compared to all other sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while NL and NM sites showed the lowest values. Conversely, sand content was highest at NL and NM sites, significantly exceeding that of NH (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Silt content showed a bimodal distribution, with NH, SH, and SK forming a high-silt group distinct from the NL and NM low-silt group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Iron speciation patterns along salinity gradients\u003c/h2\u003e \u003cp\u003eSoil iron existed predominantly as Fe(III) across all sites, with concentrations showing strong negative correlations with salinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Rhizosphere soil Fe(III) content peaked at NH (10.8 mg\u0026middot;g⁻\u0026sup1;), followed by SK (6.9 mg\u0026middot;g⁻\u0026sup1;), significantly exceeding concentrations at saline sites NM (5.2 mg\u0026middot;g⁻\u0026sup1;), NL (5.4 mg\u0026middot;g⁻\u0026sup1;), and SH (5.1 mg\u0026middot;g⁻\u0026sup1;).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConversely, Fe(II) content increased with decreasing salinity, particularly in rhizosphere soils. Low-tide flat rhizosphere soil contained 1.9 mg\u0026middot;g⁻\u0026sup1; Fe(II), compared to 2.1 mg\u0026middot;g⁻\u0026sup1; at mid-tide and 7.3 mg\u0026middot;g⁻\u0026sup1; at high-tide flats. Fe(II)/Fe(III) ratios remained below unity across all sites with NL and SH showing the highest ratios (0.99 and 0.95) and NM the lowest (0.45). These patterns reflect salinity-driven shifts in iron biogeochemical cycling and mineral stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Organic carbon distribution and environmental controls\u003c/h2\u003e \u003cp\u003eTotal organic carbon content exhibited pronounced spatial heterogeneity strongly linked to salinity gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Rhizosphere soil TOC increased significantly with decreasing salinity, peaking at NH (42.4 mg\u0026middot;g⁻\u0026sup1;) and SH (35.0 mg\u0026middot;g⁻\u0026sup1;), both substantially exceeding mid-elevation sites NM (15.2 mg\u0026middot;g⁻\u0026sup1;) and SK (18.0 mg\u0026middot;g⁻\u0026sup1;). The lowest TOC content occurred at the most saline NL site (5.0 mg\u0026middot;g⁻\u0026sup1;), reflecting salt stress impacts on plant productivity and organic matter accumulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVertical TOC distribution showed consistent depth-dependent decreases across all sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). At NH, TOC declined from 44.3 mg\u0026middot;g⁻\u0026sup1; in surface sediments (0\u0026ndash;10 cm) to 17.4 mg\u0026middot;g⁻\u0026sup1; at depth (20\u0026ndash;30 cm), with surface concentrations exceeding deeper layers by 2.5-fold. Similar patterns at NM showed a surface TOC of 47.7 mg\u0026middot;g⁻\u0026sup1; decreasing to 28.6 mg\u0026middot;g⁻\u0026sup1; at depth, indicating active carbon input and accumulation in surface horizons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Iron-bound organic carbon: content and preservation efficiency\u003c/h2\u003e \u003cp\u003eFe-OC content in rhizosphere soils increased substantially with decreasing salinity, ranging from 4.0 mg\u0026middot;g⁻\u0026sup1; at the most saline NL site to 15.2 mg\u0026middot;g⁻\u0026sup1; at SH and 13.7 mg\u0026middot;g⁻\u0026sup1; at NH (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This 3.8-fold variation demonstrates the profound impact of salinity on iron-carbon binding capacity. Intermediate sites SK (5.5 mg\u0026middot;g⁻\u0026sup1;) and NM (8.2 mg\u0026middot;g⁻\u0026sup1;) showed correspondingly intermediate Fe-OC levels. Remarkably, the proportion of total organic carbon existing as Fe-OC (\u003cem\u003ef\u003c/em\u003e\u003csub\u003eFe\u0026minus;OC\u003c/sub\u003e) exhibited opposite trends to absolute Fe-OC content (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The most saline NL site showed the highest \u003cem\u003ef\u003c/em\u003e\u003csub\u003eFe\u0026minus;OC\u003c/sub\u003e (61.8%), significantly exceeding fresher sites NH (34.2%), SH (45.3%), and SK (34.0%). This pattern indicates that while salt stress reduces absolute Fe-OC accumulation, it enhances the relative importance of iron-mediated carbon preservation mechanisms. Vertical distribution analysis revealed depth-dependent Fe-OC decreases consistent with TOC patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). At SH, Fe-OC declined from 42.1 mg\u0026middot;g⁻\u0026sup1; in surface sediments to 14.0 mg\u0026middot;g⁻\u0026sup1; at depth, with surface concentrations exceeding deeper layers by 3-fold. Anomalous increases in Fe-OC at 10\u0026ndash;20 cm depth at NM and SK sites suggest complex redox-driven redistribution processes in transitional environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Microbial diversity patterns and functional redundancy\u003c/h2\u003e \u003cp\u003eMetagenomic sequencing revealed striking patterns of taxonomic versus functional diversity across the salinity gradient (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Species diversity varied significantly among sites, with NH exhibiting the highest diversity (Shannon: 5.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, Chao1: 16,344\u0026thinsp;\u0026plusmn;\u0026thinsp;85) and NL the lowest (Shannon: 4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02). This taxonomic variation likely reflects differential environmental filtering along the salinity-redox gradient. In remarkable contrast, functional gene diversity remained virtually constant across all sites (Shannon: 7.69\u0026ndash;7.74, Chao1: 6,000\u0026ndash;6,400), despite significant taxonomic turnover. CAZy carbohydrate-active enzyme diversity also showed minimal spatial variation (Shannon: 5.72\u0026ndash;5.76).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTaxonomic and functional diversity indices of microbial communities at five sampling sites along the tidal flat gradient\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNR (species level)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eKEGG (KO level)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eCAZy (ec level)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved Species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eObserved Species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eObserved Species\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14281\u0026thinsp;\u0026plusmn;\u0026thinsp;806\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13416\u0026thinsp;\u0026plusmn;\u0026thinsp;315\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6113\u0026thinsp;\u0026plusmn;\u0026thinsp;111\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6035\u0026thinsp;\u0026plusmn;\u0026thinsp;62\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e771\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e769\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16044\u0026thinsp;\u0026plusmn;\u0026thinsp;142\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14788\u0026thinsp;\u0026plusmn;\u0026thinsp;202\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6348\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6257\u0026thinsp;\u0026plusmn;\u0026thinsp;29\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e786\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e780\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16345\u0026thinsp;\u0026plusmn;\u0026thinsp;96\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15260\u0026thinsp;\u0026plusmn;\u0026thinsp;112\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6396\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6289\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e777\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e776\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15967\u0026thinsp;\u0026plusmn;\u0026thinsp;210\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14753\u0026thinsp;\u0026plusmn;\u0026thinsp;380\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6343\u0026thinsp;\u0026plusmn;\u0026thinsp;49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6229\u0026thinsp;\u0026plusmn;\u0026thinsp;63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e780\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e776\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16160\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15024\u0026thinsp;\u0026plusmn;\u0026thinsp;107\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6377\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6271\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e779\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e777\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis reinforced these diversity patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Functional gene profiles showed stronger environmental differentiation (PC1 scores: -74.2 to +\u0026thinsp;31.5) compared to taxonomic composition, indicating that environmental gradients exert stronger selective pressure on metabolic capabilities than species identity. This functional-based community assembly ensures ecosystem resilience by maintaining critical biogeochemical processes regardless of taxonomic composition changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Functional gene expression and environmental drivers\u003c/h2\u003e \u003cp\u003eMetagenomic analysis revealed distinct functional zonation patterns across the tidal elevation gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Iron oxidation genes (\u003cem\u003ecoxA\u003c/em\u003e, \u003cem\u003ecoxB\u003c/em\u003e, \u003cem\u003ectaC\u003c/em\u003e, \u003cem\u003ectaD\u003c/em\u003e, \u003cem\u003eCYTB\u003c/em\u003e, \u003cem\u003epetA\u003c/em\u003e, \u003cem\u003epetB\u003c/em\u003e) showed significantly elevated abundances at saline sites NL and SK, aligning with oxidizing conditions that favor iron oxide formation. Carbon decomposition genes (\u003cem\u003ebglX\u003c/em\u003e, \u003cem\u003eAMY\u003c/em\u003e, \u003cem\u003eamyA\u003c/em\u003e, \u003cem\u003emalS\u003c/em\u003e, E3.2.1.6) were enriched 1.3\u0026ndash;2.1 fold at NL, suggesting enhanced organic matter degradation in frequently flooded, saline environments. Carbon fixation genes (\u003cem\u003erbcL\u003c/em\u003e, \u003cem\u003ecbbL\u003c/em\u003e, \u003cem\u003eppc\u003c/em\u003e, \u003cem\u003ePRK\u003c/em\u003e, \u003cem\u003eIDH\u003c/em\u003e) reached maximum expression at the transitional SK site (1.0-1.4 fold higher), reflecting optimal conditions for autotrophic carbon fixation in estuarine environments with moderate salinity and nutrient availability. This functional zonation represents evolutionary optimization of metabolic strategies for distinct environmental niches.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis identified specific environmental drivers of functional gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Iron reduction gene \u003cem\u003enuoC\u003c/em\u003e showed highly significant negative correlation with salinity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while nitrate reductases (\u003cem\u003enarG\u003c/em\u003e, \u003cem\u003enarZ\u003c/em\u003e, \u003cem\u003enxrA\u003c/em\u003e) exhibited positive correlations with Fe\u0026sup2;⁺ concentrations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Iron oxidation genes correlated positively with salinity but negatively with Fe-OC content, suggesting competition between iron oxidation activity and carbon preservation efficiency under high ionic strength conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Mechanistic pathways revealed by structural equation modeling\u003c/h2\u003e \u003cp\u003eThe final four-module SEM demonstrated excellent fit to the data (χ\u0026sup2; = 92.45, df\u0026thinsp;=\u0026thinsp;57, p\u0026thinsp;=\u0026thinsp;0.002; CFI\u0026thinsp;=\u0026thinsp;0.95, TLI\u0026thinsp;=\u0026thinsp;0.94, RMSEA\u0026thinsp;=\u0026thinsp;0.07, SRMR\u0026thinsp;=\u0026thinsp;0.07), explaining substantial variance in Fe-OC formation (R\u0026sup2; = 0.67) and validating our conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSalinity emerged as the dominant environmental control, exhibiting strong negative effects on carbon fixation genes (β = -0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and carbon decomposition genes (β = -0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Redox potential showed contrasting effects, negatively impacting carbon fixation genes (β = -0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) while promoting iron oxidation genes (β = +0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating redox-dependent metabolic regulation. pH specifically enhanced carbon decomposition genes (β = +0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while moisture promoted iron reduction genes (β = +0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The microbial-biogeochemical cascade operated through clear mechanistic pathways. Carbon fixation genes significantly contributed to TOC accumulation (β = +0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), representing the primary biological carbon input pathway. Iron oxidation genes strongly promoted iron oxide formation (β = +0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and increased Fe\u0026sup3;⁺/Fe\u0026sup2;⁺ ratios (β = +0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while iron reduction genes decreased this ratio (β = -0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), establishing direct links between microbial metabolism and mineral formation.\u003c/p\u003e \u003cp\u003eTotal organic carbon emerged as the dominant predictor of Fe-OC formation (β = +0.731, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accounting for 53% of explained variance and confirming carbon availability as fundamental to organo-mineral interactions. Iron oxides contributed significantly as the binding matrix (β = +0.301, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Importantly, salinity exhibited significant direct negative effects on Fe-OC formation (β = -0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) independent of microbial pathways, indicating additional geochemical interference mechanisms.\u003c/p\u003e \u003cp\u003eThe strongest mechanistic pathway operated through salinity \u0026rarr; iron oxidation genes \u0026rarr; iron oxides \u0026rarr; Fe-OC, explaining 23% of total Fe-OC variance. This reveals that salinity-induced changes in iron-oxidizing microbial activity fundamentally alter the iron mineral template available for carbon stabilization, providing a mechanistic explanation for observed Fe-OC decreases along the salinity gradient.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Environmental controls on iron speciation\u003c/h2\u003e \u003cp\u003eIron elements enriched in soil exhibit significant dynamic changes in wetland environments due to their active redox properties. Periodic flooding in tidal flat wetlands leads to frequent alternation between aerobic and anaerobic conditions, driving continuous oxidation and reduction reactions of iron elements in soil (Lei et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study found that iron in natural tidal flat wetland soils existed mainly as Fe(III). However, with decreasing tidal flat elevation, prolonged flooding time, and increasing salinity, Fe(III) concentration showed a significant decreasing trend, while Fe(II) content increased with rising salinity. This change may result from soil hydrological condition fluctuations caused by flooding and salinity increases, leading to iron loss in tidal flat soils.\u003c/p\u003e \u003cp\u003eSoil particle size distribution also plays a critical role in iron speciation and Fe-OC formation. Our results showed that the NH site exhibited significantly higher clay content compared to all other sites, while NL and NM sites showed the lowest values. This spatial pattern directly influences iron dynamics through multiple mechanisms. Clay minerals provide abundant surface area and reactive sites for iron oxide precipitation and organic carbon adsorption (Tomb\u0026aacute;cz et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The higher clay content at NH coincides with both higher Fe(III) concentrations and greater Fe-OC content, suggesting that fine-grained sediments enhance iron mineral stability and carbon binding capacity. Conversely, the sandy substrates at low-tide flats (NL, NM) facilitate water drainage and oxygen penetration during low tide but also promote iron leaching during flooding periods. The transition from sandy compositions at exposed flats to clay-dominated substrates at elevated sites reflects progressive sediment accretion and ecosystem development, creating a physical framework that supports increasingly complex iron-carbon interactions.\u003c/p\u003e \u003cp\u003eThe observed iron speciation patterns align with recent advances in understanding microbial iron cycling mechanisms. Soil Eh is a key controlling factor for iron form transformation (Spiteri et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e): in well-aerated high-tide flat environments, iron exists mainly as stable Fe(III) through the activity of iron-oxidizing genes such as cytochrome c oxidase subunits and cytochrome b. These genes show significantly higher abundances at low tide and estuarine sites, aligning with oxidizing conditions that favor iron oxide formation. In low-tide flat areas near the ocean, prolonged flooding creates anaerobic conditions that promote dissimilatory iron reduction, with soil microorganisms expressing iron reduction genes (such as nitrate reductases and NADH dehydrogenase subunits) and using Fe(III) as electron acceptors to convert it to more soluble Fe(II) (Johnston et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLiu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that soil EC was significantly positively correlated with active iron content. However, in this study, low-tide flat areas had reduced soil EC due to prolonged seawater immersion, which instead promoted iron activation and migration, leading to decreased active iron content (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fe(II)/Fe(III) ratios are commonly used to characterize soil redox environments (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e): ratios\u0026thinsp;\u0026lt;\u0026thinsp;1 indicate oxidizing environments, and \u0026gt;\u0026thinsp;1 indicate reducing environments. This study's results showed that SH site soil had the highest Fe(II)/Fe(III) ratio, approaching the reducing type, while NH and SK sites had ratios around 0.2, reflecting strong oxidizing environments. Additionally, soil particle size was positively correlated with Fe(II)/Fe(III) ratios, with finer particles showing smaller ratios (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), consistent with the trend of gradually finer soil particles and environmental transition from reducing to oxidizing during tidal flat development from bare flats to estuaries. Liu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also found that SOC content was significantly positively correlated with active iron, and SOC reduction may be an important driver of iron loss (Giannetta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, compared to low-carbon environments in low-tide flats, high-tide flat soils rich in organic matter are more conducive to iron stability and retention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Salinity control on soil organic carbon and iron-bound organic carbon: Mechanistic pathways\u003c/h2\u003e \u003cp\u003eThis study observed that in different tidal flat mangrove rhizosphere soils, TOC content increased significantly with decreasing salinity. Wetland plants are the main source of primary carbon in soil (Cragg et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and their biomass is significantly inhibited by salt stress, leading to decreased SOC content (Naskar and Palit, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Howard et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Bai et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found in Minjiang estuary intertidal wetland studies that increased salinity led to reduced plant biomass, thereby decreasing soil OC storage. Jiang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) further indicated in Chongming Dongtan wetland studies that soil salinity within certain ranges (\u0026lt;\u0026thinsp;15\u0026permil;) promoted SOC fixation, but beyond this threshold, salinity had significant negative effects on SOC content, consistent with this study's results.\u003c/p\u003e \u003cp\u003eOur structural equation modeling reveals that salinity operates as a master environmental variable controlling Fe-OC formation through sophisticated, multi-level pathways. The mechanistic pathway salinity \u0026rarr; iron oxidation genes \u0026rarr; iron oxides \u0026rarr; Fe-OC explains 23% of total Fe-OC variance, demonstrating that salinity-induced changes in iron-oxidizing microbial activity fundamentally alter the iron mineral template available for carbon stabilization. This mechanistic clarity represents a significant advance beyond previous correlative studies and establishes microbial metabolism as a critical link between environmental conditions and carbon sequestration outcomes.\u003c/p\u003e \u003cp\u003eIron mineral content and iron-bound organic carbon (Fe-OC) content were consistent with SOC change trends, both decreasing significantly with increasing salinity. Previous research confirmed significant positive correlations between iron mineral content and organic carbon content, with highly consistent distributions (Zhao et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Recent advances in understanding iron-carbon interactions reveal that salinity gradients fundamentally alter iron mineral transformations through iron-sulfur competition. High salinity promotes accumulation of humic substances (HS) in soil through flocculation effects. Research in Japanese mangroves found that when using pure water to extract soil, HS concentration significantly increased, but adding artificial seawater caused approximately 70% of HS to re-precipitate, indicating that salinity helps retain HS in soil (Kida et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, high salinity (30 g/L) can significantly alter root exudate composition, reducing crystalline and amorphous iron (hydr)oxides content while increasing composite iron (hydr)oxide levels, thereby reducing iron crystallinity in soil. Under such conditions, Fe-OC bonds show higher stability and resistance to salt stress compared to non-rhizosphere soils (Lei et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLiu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that root Fe(III) plaque abundance responds differentially across salinity levels, with soil chloride concentrations emerging as the best predictor of iron reduction rates in coastal wetlands. Salinity and flooding are key factors in the transformation of iron minerals: increased seawater salinity accompanies increased ionic strength (such as Na⁺, S\u0026sup2;⁻, Mg\u0026sup2;⁺, and Ca\u0026sup2;⁺), and these ions compete with organic carbon for binding sites on iron mineral surfaces, inhibiting Fe adsorption of OC (Tomaszewski et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The formation of iron sulfide complexes under high salinity conditions removes reactive iron from the carbon stabilization pool, with continuous saltwater exposure promoting FeS formation that renders iron unavailable for organic carbon protection (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, increased ionic strength may enhance microbial reduction activity on iron mineral surfaces (Weston et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), reducing Fe-OC stability. Furthermore, decreased soil Eh further changes iron mineral forms and their adsorption kinetics for SOC, affecting organic carbon stability (Kleber et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These salinity-driven transformations follow predictable patterns, with poorly crystalline iron phases (ferrihydrite, lepidocrocite) providing superior carbon-binding capacity compared to crystalline counterparts, but saltwater intrusion accelerates mineral crystallization and reduces carbon retention efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Spatial patterns and microbial mechanisms: Rhizosphere effects and vertical distribution\u003c/h2\u003e \u003cp\u003eThis study found that the NM site's rhizosphere soil Fe-OC content was significantly lower than bulk soil, while the NH site showed the opposite pattern. Mangrove root systems play key roles in iron mineral cycling, with the rhizosphere serving as the interface for plant-soil-microorganism interactions, driving iron oxidation-reduction processes in tidal wetland soils (Luo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This differential pattern reveals the complexity and environmental dependency of rhizosphere effects, which can be explained through the differential distribution of microbial functional genes observed in our metagenomic analysis.\u003c/p\u003e \u003cp\u003eOur data demonstrate that the differences in Fe-OC distribution between rhizosphere and bulk soils are closely related to salinity gradients. At the low-salinity NH site, rhizosphere Fe-OC content (13.7 mg\u0026middot;g⁻\u0026sup1;) exceeded that of corresponding bulk soils, indicating that under favorable environmental conditions, the rhizosphere creates a microenvironment conducive to Fe-OC formation. This finding aligns with previous studies showing that rhizosphere soils typically contain 3\u0026ndash;5 times higher Fe-OC concentrations than bulk soils (Duan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, at the moderate-salinity NM site, rhizosphere Fe-OC content decreased, which corresponds with our observed changes in iron oxidation gene expression patterns under salt stress.\u003c/p\u003e \u003cp\u003eFunctional gene analysis further supports this explanation. At the NH site, the abundance of carbon fixation genes was highest, while iron oxidation genes also maintained relatively high levels. This coordinated expression of functional genes provided favorable conditions for rhizosphere Fe-OC accumulation. Our correlation analysis revealed that iron oxidation genes showed positive correlations with Fe-OC content in low-salinity environments, while exhibiting negative correlations under high-salinity conditions, indicating that salinity is a key environmental factor regulating rhizosphere effects.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying these rhizosphere-bulk soil differences involve complex biogeochemical processes. Salt stress changes root exudate composition and increases secretion to enhance plant adaptability (Zhou et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our structural equation model revealed that changes in rhizosphere microbial community structure serve as the critical pathway connecting environmental conditions with Fe-OC formation. The decrease in rhizosphere soil Fe-OC content with increasing salinity may be driven by the following mechanisms: (1) Salt stress suppresses carbon fixation gene expression, reducing organic carbon input; (2) although iron oxidation gene activity is enhanced under high ionic strength environments, the binding efficiency between formed iron oxides and organic carbon decreases.\u003c/p\u003e \u003cp\u003eThese findings indicate that mangrove rhizosphere effects do not simply promote Fe-OC formation but are regulated by complex environmental-microbial-plant feedback. This study provides molecular-level evidence for these regulatory mechanisms, offering new insights into understanding the spatial heterogeneity of mangrove carbon sink functions and their responses to environmental gradients.\u003c/p\u003e \u003cp\u003eVertical patterns also showed that TOC content decreased with increasing soil depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), particularly evident in high-tide flats: The 0\u0026ndash;10 cm surface layer TOC content was approximately 1.3 times that of the 10\u0026ndash;20 cm layer and 2.4 times that of the 20\u0026ndash;30 cm layer. Tidal flat wetland soil organic carbon usually accumulates in surface layers and decreases with depth (Zhou et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), related to vegetation residue decomposition, tidal input of particulate matter, and concentrated microbial activity in surface layers (Bi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Chen et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) indicated that mangrove restoration significantly enhanced surface SOC content and storage, consistent with this study.\u003c/p\u003e \u003cp\u003eFe-OC content also decreased with depth, with obvious trends in high-tide flats, but abnormal increases occurred at 10\u0026ndash;20 cm in mid-tide flats. This vertical distribution pattern reflects sophisticated biogeochemical stratification that maximizes carbon preservation efficiency. Surface sediments (0\u0026ndash;10 cm) harbor the highest diversity of both carbon fixation and iron oxidation genes, creating an active zone where fresh organic matter rapidly associates with newly formed iron oxides. This phenomenon explains why Fe-OC content in surface layers was 1.95\u0026ndash;3.08 times higher than deeper sediments at high-tide sites. The co-localization of carbon production and iron mineral formation represents an optimal configuration for carbon sequestration.\u003c/p\u003e \u003cp\u003eBelow 10 cm depth, the shift toward anaerobic metabolism fundamentally alters iron-carbon dynamics. The dominance of iron reduction genes in subsurface layers, particularly nitrate reductases showing positive correlations with Fe\u0026sup2;⁺ concentrations, indicates active iron mineral dissolution that releases previously bound carbon. This phenomenon explains the anomalous increases in Fe-OC content at 10\u0026ndash;20 cm depth in mid-tide flats, where fluctuating water tables create alternating oxidation-reduction cycles that repeatedly form and dissolve iron-carbon complexes. Such redox oscillations, while destabilizing individual Fe-OC associations, may ultimately enhance long-term carbon preservation by redistributing organic matter throughout the soil profile.\u003c/p\u003e \u003cp\u003eHuang et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that TOC content was the main determinant of Fe-OC concentration, with binding mechanisms controlled by both TOC and active iron. Fe-OC proportions to TOC were positively correlated with active iron, indicating that iron mineral roles in OC accumulation were limited by active iron concentrations. Higher Fe-OC proportions in surface soils highlighted the key role of iron minerals in regulating organic carbon stability and long-term storage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Functional redundancy, environmental thresholds, and management implications\u003c/h2\u003e \u003cp\u003eOur metagenomic analysis revealed a fundamental principle of microbial ecology in mangrove systems: functional redundancy ensures ecosystem stability despite environmental perturbations. The striking contrast between high taxonomic variability and stable functional diversity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrates that mangrove microbial communities maintain essential biogeochemical functions through multiple backup systems.\u003c/p\u003e \u003cp\u003eThis finding aligns with recent studies showing that coastal wetland microbiomes exhibit extensive functional redundancy as an adaptation strategy to fluctuating environmental conditions (Louca et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The functional redundancy mechanism operates through phylogenetically distinct taxa performing equivalent ecological roles. For example, carbon decomposition genes (\u003cem\u003ebamA\u003c/em\u003e, \u003cem\u003ealkB\u003c/em\u003e, \u003cem\u003eoah\u003c/em\u003e) are distributed among different bacterial genera (\u003cem\u003eAlcanivorax\u003c/em\u003e, \u003cem\u003eMarinobacter\u003c/em\u003e), ensuring continued hydrocarbon degradation even when individual species are eliminated by environmental stress. Similarly, carbon fixation genes (\u003cem\u003ecbbL\u003c/em\u003e, \u003cem\u003ecbbM\u003c/em\u003e) are present in various microaerophilic iron-oxidizing bacteria, maintaining CO₂ fixation capacity through the Calvin-Benson-Bassham pathway regardless of specific taxonomic composition changes.\u003c/p\u003e \u003cp\u003eThe PCA results offer theoretical explanations for how functional redundancy operates across environmental gradients. The stronger separation of samples based on functional genes (PC1 scores: -74.2 to 31.5) compared to taxonomic composition indicates that environmental filtering primarily acts on metabolic capabilities rather than species identity. This functional-based community assembly ensures that critical processes\u0026mdash;iron oxidation/reduction, carbon fixation/decomposition\u0026mdash;persist even when individual taxa are eliminated by environmental stress. For Fe-OC formation, this means that the ecosystem maintains its carbon sequestration capacity through alternative microbial pathways, providing resilience against environmental changes in salinity and flooding regimes.\u003c/p\u003e \u003cp\u003eThe implications of functional redundancy extend to ecosystem management strategies. Rather than focusing on preserving specific microbial taxa, conservation efforts should aim to maintain the environmental conditions that support diverse functional guilds. Our data suggest that moderate environmental heterogeneity, as observed at the NH site with its intermediate salinity and optimal organic matter content, promotes both taxonomic and functional diversity, creating the most resilient system for long-term carbon storage.\u003c/p\u003e \u003cp\u003eThe non-linear response of Fe-OC to salinity gradients suggests the existence of critical thresholds beyond which carbon sequestration mechanisms fail. The sharp decline in Fe-OC content above 2.0\u0026permil; salinity, despite maintaining 56\u0026ndash;62% of TOC as Fe-OC, indicates a salinity tipping point where ionic interference overwhelms iron-carbon binding capacity. This threshold corresponds closely with the shift in microbial community composition revealed by PCA, where low-tide flat samples separate distinctly from other sites along PC1.\u003c/p\u003e \u003cp\u003eResearch in Japanese mangroves has shown that salinity effects on organic carbon accumulation follow complex patterns. High salinity can promote soil organic carbon accumulation through direct physical-chemical processes, particularly through flocculation effects that help retain humic substances in soil (Kida et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, our study reveals that beyond the 2.0\u0026permil; threshold, these beneficial effects are overwhelmed by negative impacts on iron-carbon binding mechanisms. The contrasting results between studies using 30 g/L salinity (which enhanced Fe-OC stability) and our findings suggest that salinity effects may be species-specific and highly dependent on local environmental conditions.\u003c/p\u003e \u003cp\u003eThe gene-environment correlations provide molecular markers for approaching these thresholds. The highly significant negative correlation between nuoC and salinity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggests this gene could serve as an early warning indicator of osmotic stress impacts on microbial metabolism. Similarly, the shift from carbon fixation to decomposition gene dominance marks the transition from carbon-accumulating to carbon-losing systems. Monitoring these functional gene abundances could provide real-time assessment of ecosystem health and carbon sequestration capacity.\u003c/p\u003e \u003cp\u003eEnvironmental changes that increase salinity variability and flooding frequency may push mangrove systems across these thresholds more frequently (Saintilan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our data suggest that maintaining salinity below 2.0\u0026permil; through freshwater management or strategic mangrove placement could preserve optimal conditions for Fe-OC formation. However, the functional redundancy observed across sites provides hope that ecosystems can adapt to gradual changes, as long as extreme events don't overwhelm the system's buffering capacity.\u003c/p\u003e \u003cp\u003eThe identification of specific functional gene markers for ecosystem health provides new tools for blue carbon management. Rather than focusing solely on mangrove area preservation, conservation efforts should target the maintenance of environmental conditions that support diverse microbial functional guilds. The finding that the abundance of iron oxidation genes correlates with Fe-OC formation capacity suggests that monitoring these genes could provide early warning systems for carbon sequestration decline.\u003c/p\u003e \u003cp\u003eOur findings indicate that maintaining optimal salinity ranges below 2.0 mg\u0026middot;L⁻\u0026sup1; through appropriate hydrological management can maximize the expression of iron oxidation genes and Fe-OC formation. This threshold aligns with previous observations that soil salinity within certain ranges promotes SOC fixation, but beyond this threshold, salinity has significant negative effects on SOC content (Jiang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Conservation strategies should focus on preserving environmental heterogeneity that supports diverse functional guilds rather than specific taxonomic groups, as functional redundancy provides greater ecosystem resilience than taxonomic diversity alone. Molecular monitoring using iron oxidation gene abundance and carbon fixation gene expression offers real-time indicators of ecosystem carbon sequestration capacity, enabling adaptive management responses before irreversible degradation occurs.\u003c/p\u003e \u003cp\u003eManagement approaches should also develop strategies that leverage natural microbial carbon fixation processes, particularly those involving microaerophilic FeOB that simultaneously bind carbon and form iron oxides (Dong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The functional redundancy demonstrated in our study provides optimism for ecosystem resilience under moderate environmental changes but also highlights the importance of maintaining the environmental conditions that support this redundancy. As environmental pressures intensify in coastal areas, understanding and preserving these sophisticated microbial mechanisms becomes increasingly critical for maintaining the vital carbon sink services provided by mangrove ecosystems.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study provides an integrated biogeochemical and metagenomic analysis of iron-bound organic carbon (Fe-OC) formation mechanisms in mangrove rhizosphere soils across natural salinity gradients. Our findings reveal that salinity operates as a master environmental control on Fe-OC formation through a dominant microbial-mediated pathway (salinity \u0026rarr; iron oxidation genes \u0026rarr; iron oxides \u0026rarr; Fe-OC), explaining 23% of total variance and establishing microbial metabolism as the critical link between environmental conditions and carbon sequestration. Despite significant taxonomic turnover across environmental gradients, functional redundancy ensures ecosystem resilience by maintaining stable functional diversity, which is essential for supporting biogeochemical processes. However, critical salinity thresholds (~\u0026thinsp;2.0\u0026permil;) exist, beyond which the mechanisms for Fe-OC formation begin to fail; paradoxically, carbon preservation efficiency increases even as the absolute content declines. Mangrove rhizospheres intensify these iron-carbon coupling processes through plant-soil-microbe interactions, creating biogeochemical hotspots that provide localized protection against environmental stress.\u003c/p\u003e \u003cp\u003eThese mechanistic insights transform blue carbon management from area-based conservation to process-based approaches targeting environmental conditions that support diverse microbial functional guilds. Molecular monitoring of functional gene abundances offers new tools for real-time ecosystem assessment and early warning of degradation, while maintaining optimal salinity ranges below critical thresholds through hydrological management could preserve favorable Fe-OC formation conditions. As climate change intensifies coastal stressors, understanding and preserving these sophisticated microbial mechanisms becomes increasingly critical for maintaining the vital carbon sink services provided by mangrove ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant Nos. 42477130 and 42141016) and the National Key R\u0026amp;D Program of China (Grant No. 2023YFE0113100).\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation, field sampling, data collection, and analysis were performed by Ying Lei and Lv Gong. The first draft of the manuscript was written by Ying Lei and Zhongzheng Yan. Xiuzhen Li provided critical feedback and resources. Zhongzheng Yan supervised the project, secured funding, and revised the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant number 42477130], National Key R\u0026amp;D Program of China [grant number 2023YFE0113100], and Key Projects of National Natural Science Foundation of China [grant number 42141016].\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBai J, Luo M, Yang Y, Xiao S, Zhai Z, Huang J (2021) Iron-bound carbon increases along a freshwater\u0026ndash;oligohaline gradient in a subtropical tidal wetland. 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Int J Mol Sci 19:252\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Nanliu River, mangrove, salinity, iron-bound organic carbon, metagenomics, structural equation modeling","lastPublishedDoi":"10.21203/rs.3.rs-8936246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8936246/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 serve as critical blue carbon sinks, yet the microbial mechanisms governing iron-bound organic carbon (Fe-OC) formation under varying salinity conditions remain poorly understood. This study aims to elucidate the microbial-mediated pathways controlling Fe-OC formation in \u003cem\u003eAegiceras corniculatum\u003c/em\u003e rhizosphere soils across a natural salinity gradient (1.5\u0026ndash;2.4\u0026permil;).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed an integrated biogeochemical and shotgun metagenomic approach to analyze rhizosphere and bulk soils from the Nanliu River Estuary, China. Structural equation modeling (SEM) was utilized to identify the dominant mechanistic pathways linking environmental factors, microbial metabolism, and Fe-OC formation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnder increasing salt stress, absolute Fe-OC content declined from 15.2 to 4.0 mg\u0026middot;g⁻\u0026sup1;, yet its proportion within total organic carbon increased from 34.2% to 61.8%. Metagenomic analysis revealed stable functional gene diversity despite significant taxonomic turnover, demonstrating inherent functional redundancy. Iron oxidation genes were enriched in saline flats, while carbon fixation genes concentrated in fresher sites. SEM identified salinity as the master environmental control (R\u0026sup2; = 0.67), operating primarily through a dominant pathway: salinity \u0026rarr; iron oxidation genes \u0026rarr; iron oxides \u0026rarr; Fe-OC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMangrove root metabolism and microbial functional genes synergistically mediate soil iron-carbon binding. Salinity acts as the primary environmental control through both direct geochemical effects and indirect pathways via microbial community restructuring, highlighting the importance of functional redundancy in maintaining ecosystem resilience under environmental stress.\u003c/p\u003e","manuscriptTitle":"Microbial-mediated mechanisms of iron-bound organic carbon formation in mangrove rhizosphere soils: An integrated biogeochemical and metagenomic analysis along a salinity gradient","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 18:02:42","doi":"10.21203/rs.3.rs-8936246/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-05-05T07:06:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-03-29T04:20:38+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T11:11:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2026-02-23T09:06:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T00:14:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2026-02-21T20:07:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"100c6877-17ef-4e5b-b73b-433d32868b32","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Major revisions","date":"2026-05-05T07:06:34+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T11:07:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 18:02:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8936246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8936246","identity":"rs-8936246","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}