Altitudinal Variation in Soil Organic Carbon Turnover: Decoupling Climate and Edaphic Drivers in Changbai Mountain Forest Wetlands

Altitudinal Variation in Soil Organic Carbon Turnover: Decoupling Climate and Edaphic Drivers in Changbai Mountain Forest Wetlands | 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 Altitudinal Variation in Soil Organic Carbon Turnover: Decoupling Climate and Edaphic Drivers in Changbai Mountain Forest Wetlands Kun Zhang, Tingting Wu, Tianjiao Wang, Mengfei Dou, Cui Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6852862/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding how soil and climatic factors jointly regulate soil organic carbon (SOC) turnover in forested wetlands is crucial for assessing their role as carbon sources or sinks. This study investigated δ¹³C-based carbon dynamics along an altitudinal gradient (700–1,818 m) in the Changbai Mountain to identify key environmental drivers and inform regional carbon management strategies. We analyzed δ¹³C and β values in soil and litter samples across six elevations, alongside SOC and nutrient concentrations (TN, TP, TK), using principal component analysis to determine the dominant controls on SOC turnover. Results showed that SOC concentrations declined with depth, while δ¹³C values increased. Notably, the 700 m site exhibited unusually high SOC and δ¹³C enrichment, likely due to persistent waterlogging. β values peaked at mid-elevations (700–1,300 m) and were consistently higher in litter than in soil, indicating more active turnover. Edaphic factors accounted for 86% of the variation in β values, far exceeding the influence of climate variables (26.8%). Overall, mid-elevation wetlands exhibited the most favorable conditions for SOC turnover due to optimal moisture and nutrient availability, while high-elevation zones (> 1,500 m) functioned as cold-driven carbon sinks. δ¹³C is demonstrated to be an effective tracer of SOC turnover, underscoring the need for hydrological restoration and nutrient management in mid-altitude wetland ecosystems. Stable carbon isotopes Altitudinal gradient Soil organic matter turnover Forest wetlands Edaphic controls Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Wetlands function as critical carbon sinks, storing approximately 20–30% of global soil organic carbon (SOC) while occupying only 5–8% of the Earth's terrestrial surface (Lal 2008 ). Forest wetlands—the largest and most ecologically significant wetland subtype—act as transitional zones between terrestrial and aquatic ecosystems (Bridgham et al. 2013 ). Through the modulation of evapotranspiration and greenhouse gas exchange, forest wetlands serve as critical nodes in the terrestrial carbon fluxes and play a pivotal role in regulating climate at both regional and global scales (Davidson 2022). Despite their importance in carbon sequestration, forest wetlands face unprecedented challenges under global climate change. Climate change affects wetland carbon cycling through complex interactions among environmental factors (Paré et al. 2022 ). In forested wetlands, rising groundwater levels trigger shifts in plant communities, soil properties, and biodiversity, reshaping carbon cycle dynamics (Mitsch et al. 2013 ). Climate change affects organic carbon decomposition and sequestration by altering soil microbial communities (Dong et al. 2021 ; Zhao et al. 2024 ). Temperature variability and shifting precipitation patterns significantly influence water balance and carbon fluxes in forested wetlands, particularly in regions with pronounced seasonality (Zhao et al. 2024 ). However, the mechanisms by which soil and climatic factors regulate organic carbon transformation along altitudinal gradients in forest wetlands are still poorly understood. Addressing these knowledge gaps is critical for improving terrestrial carbon feedback models and informing effective climate mitigation strategies. Long-term carbon pool dynamics, characterized by disturbance events, gradual accumulation, and high spatiotemporal variability, remain difficult to quantify using conventional approaches such as destructive sampling. Stable carbon isotope techniques offer a promising alternative. By precisely tracing the dynamics of organic carbon through carbon stable Isotope composition (δ¹³C) signatures in the plant-soil system, these approaches offer crucial mechanistic insights into carbon cycling processes at the ecosystem scale (McCloskey et al. 2021 ). The majority of soil organic matter comes from litter, and vegetation signatures are usually reflected in soil δ¹³C readings (Bradford et al. 2016 ; Cotrufo et al. 2013 ). Environmental variables such as temperature that influence litter δ¹³C may also affect soil δ¹³C composition (Cernusak et al. 2016 ; Hicks Pries et al. 2017 ). The relevance of these factors in soil δ¹³C dynamics is still up for debate, although temperature impacts on plant δ¹³C are often limited (Jia et al. 2016 ). This discrepancy implies that a variety of abiotic factors influences soil δ¹³C patterns in addition to temperature, such as precipitation regimes (Bradford et al. 2016 ; Delgado-Baquerizo et al. 2016 ), soil C/N ratios (Wang et al. 2015 ), pH gradients (Wang et al. 2013 ; Yuan and Chen 2015 ), and other pedochemical characteristics. The β coefficient, derived from the linear regression of log-transformed SOC against δ¹³C values, serves as a robust proxy for SOM turnover by quantifying isotopic fractionation per unit of carbon loss (Wang et al. 2017 ). By analyzing several forest locations, earlier research has shown that β coefficients are effective at indicating the degree of soil organic matter (SOM) decomposition (Garten et al. 2000 ). There is a crucial knowledge vacuum regarding SOM dynamics in forested wetlands, particularly primary forest wetlands, as the majority of recent research has been on terrestrial forests and grasslands (Angst et al. 2019 ; Chari and Taylor 2022 ; Pan et al. 2017 ). Changbai Mountain(CBS) exhibits pronounced vertical zonation due to its distinctive geographical and environmental conditions. The terrain causes increased precipitation, and the environmental gradient is steep, with the temperature decreasing by about 0.65°C for every 100 meters of elevation gain (Cheng et al. 2010 ; Sigalue et al. 2017 ). The peatlands here contribute significantly to the global carbon cycle, with an average carbon accumulation intensity of up to 164.43 tons/km² and a carbon accumulation rate of 38.96 g/(A m²) (Liu et al. 2024 ). However, CBS's forest wetlands have not received enough attention in the field of forest wetland research, which has previously focused mostly on the Greater and Lesser Xing'an Mountains (Gu et al. 2018 ; He et al. 2021 ; Sun 2010). This study focuses on the ecologically significant yet understudied forest wetlands of CBS to investigate their unique biological mechanisms and carbon cycling dynamics. This study aims to: (1) examine the altitudinal patterns of SOC dynamics and δ 13 C fractionation across depth profiles in CBS forest wetlands; (2) identify the main drivers of SOC turnover using principal component analysis of climatic and edaphic factors in conjunction with β-value regression slopes; (3) identify carbon cycling patterns along altitudinal gradients and propose targeted conservation strategies to enhance carbon sequestration and restore hydrological function. 2. Materials and methods 2.1. Study area and soil sample collection Located in southeastern Jilin Province, China, the CBS Nature Reserve (Fig. 1 ) spans 196,465 hectares and reaches a maximum elevation of 2,744 meters. This temperate forest ecosystem of global ecological significance is situated near the China–North Korea border (127°43′–128°17′E, 41°42′–42°25′N). The region harbors one of East Asia’s most intact forest ecosystems and is recognized as a UNESCO Biosphere Reserve (Fu et al. 2016 ). The region experiences a continental monsoon climate, with mean annual temperatures ranging from 2°C to 4°C and precipitation increasing from 632 mm in lowlands to 1,154 mm at higher elevations(Liu et al. 2019 ). From deciduous broadleaf forests below 500 meters to mixed conifer-broadleaf stands (500–1,100 meters), coniferous forests (1,100–1,700 meters), Betula ermanii-dominated subalpine woodlands (1,700–2,100 meters), and alpine tundra above 2,100 meters, the vegetation exhibits clear vertical zonation (Kang et al. 2023 ). Vegetation across all elevational zones primarily consists of species utilizing the C₃ photosynthetic pathway, such as the dominant tree genera Larix, Betula, Abies, and Picea, which are typical of temperate forest ecosystems (Tan et al. 2009 ). The spatial heterogeneity of ecosystems across various elevations is significantly shaped by this prominent vertical zonation of vegetation, which is fueled by the gradient distribution of light, heat, water, and soil elements. 2.2. Soil and litter sample collection Six ecologically distinct forested wetlands were systematically selected along the northern slope of CBS, covering a broad elevation gradient (Fig. 1 ). Table 1 provides a summary of the habitat characteristics, elevation, mean annual temperature, yearly precipitation, and geographic coordinates. At 700 m and 1,000 m elevations, five 10 m × 10 m plots were established using an “S”-shaped layout due to the broad extent and relatively uniform texture of these wetlands. For the remaining four elevations, plots were arranged using the plum blossom method, with three 10 m × 10 m plots established per site. Plot numbers were determined based on wetland area and plant diversity. Within each plot, three 1 m × 1 m subplots were designated along the diagonal to record vegetation and site characteristics. At elevations above 1,300 m, shallower soil profiles necessitated finer depth intervals for sampling. At lower altitudes (700–1,300 m), deeper stratified sampling was used due to the deeper soils. Soil profiles were separated into five depth intervals between 700 and 1300 meters: 0–5 cm, 5–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm. Four depth intervals were used for sampling at higher elevations (1,514–1,818 m), when soil profiles were shallowed to 15 cm: 0–2 cm, 2–5 cm, 5–10 cm, and 10–15 cm. A total of 91 soil and 19 litter samples were collected using sterile stainless-steel augers, with rigorous contamination controls including triple-rinsing with deionized water between sites. Samples are processed within 24 hours to preserve isotopic integrity, stored at 4°C during transport, and sealed in argon-flushed polyethylene bags to minimize aerobic oxidation. Before physicochemical evaluation, post-collection procedures included mechanical grinding through 2 mm nylon sieves, air-drying at 25°C for 48 hours, and refrigeration at 4°C. Table.1 Characteristic study locations on CBS's northern slope along the height gradient. Altitude(m) Location Dominant tree species MAT(℃) MAP(mm) Habitat 700 42.04°N,128.33°E Larix olgensis , Albizia kalkora 1.28 337 Static water is present, and the earth is supersaturated. 1000 42.18°N,128.16°E Larix olgensis , Betula platyphylla 0.44 389.91 The earth is supersaturated, and there is a creek on one side. 1300 42.13°N,128.12°E Abies nephrolepis , Picea jezoensis , Larix olgensis -1.94 677.1 Seasonal wetland 1514 42.10°N,128.09°E Abies nephrolepis , Picea jezoensis , Larix olgensis -4.75 1063.38 There are numerous fallen trees at the site, which is in a valley 200 meters from the highway. 1687 42.08°N,128.07°E Abies nephrolepis , Picea jezoensis , Larix olgensis , Betula platyphylla -5.96 1170.24 A creek on the east and west sides of the boardwalk. 1818 42.06°N,128.06°E Larix olgensis , Betula platyphylla -6.59 1250.85 The earth is saturated close to the lake, with water in the center. MAP and MAT refer to mean annual precipitation and mean annual temperature, respectively. 2.3. Laboratory analyses Soil pH was measured using a glass electrode in a 1:2.5 soil-to-distilled-water suspension. A Kjeldahl distillation-autotitration system was used to calculate the total nitrogen (TN). Following digestion with hydrofluoric and perchloric acid, total phosphorus (TP) was determined colorimetrically. The concentration of potassium ions in HCL solution, as measured by atomic absorption spectrophotometry, was transformed into the total potassium (TK) content of the soil. Using elemental analysis flow mass spectrometry (912-0003, LICA, China), SOC and δ¹³C were measured using elemental analyzer–isotope ratio mass spectrometry (EA-IRMS), with an analytical precision better than 0.15‰. The abundance of δ¹³C can be calculated using the formula below: $$\:\begin{array}{c}{\text{δ}}^{\text{13}}\text{C}＝\frac{{\text{R}}_{\text{sample}}}{{\text{R}}_{\text{PDB}}}-\text{1}\times\:\text{1000}\#\left(\text{1}\right)\end{array}$$ where R sample and R PDB are the sample's and standard's 13C/12C ratios, respectively, and δ 13 C is the sample's carbon isotope ratio. When measuring δ 13 C in this manner, the standard deviation is roughly 0.2‰. The calculation of depth-dependent isotopic enrichment (Δδ¹³C) was as follows: where 60 cm for elevations ≤ 1,300 m and 15 cm for elevations > 1,300 m correspond to the deepest layer. 2.4. Data analyses Climate data for this study were obtained from the China Meteorological Administration ( http://www.cma.gov.cn/ ), based on records from nine monitoring stations along the northern slope of CBS. For each soil depth profile, log-transformed SOC concentrations and δ¹³C values were analyzed using stepwise regression, where the regression slope was defined as the β value. One-way analysis of variance (ANOVA) was performed to assess statistical significance, while Pearson correlation was used to evaluate relationships between soil δ¹³C and environmental variables. Principal component analysis (PCA) was conducted to identify the primary environmental drivers of soil carbon dynamics along the altitudinal gradient. Statistical significance was determined at p < 0.05. All statistical analyses were performed using Origin 2021 and SPSS 26.0. 3. Result 3.1. Soil depth profiles affecting δ 13 Cand β value Significant differences in SOC and δ¹³C values are observed across soil depths and elevations. As shown in Fig. 2 a, SOC concentrations decrease exponentially with depth. The mean SOC at 700 m (32.22%) was markedly higher than at other elevations (3.72–14.11%; Table 2 ), indicating an anomalous pattern of carbon accumulation. Except for 700 m a.s.l., soil δ¹³C showed progressive enrichment with depth at all elevations. Isotopic enrichment is most pronounced in surface soils (0–10 cm) and stabilizes in deeper layers (15–60 cm; Fig. 2 b). The effect of soil depth diminishes the δ¹³C enrichment with increasing depth. At 1000 m (-27.48 ± 0.69‰) and 1300 m (-27.82 ± 0.48‰), the mean δ¹³C values were significantly higher than those at other elevations ( p 0.05) between them. Litter-layer SOC (45.67–49.37%) was significantly different from soils, particularly at 700 m, where mean SOC was 18.11–28.5% higher than at other sites ( p < 0.05) (Table 2 ). Elevational variation may affect decomposition rates and promote carbon sequestration through root-derived inputs. The range of the Δδ¹³C enrichment was 2.85‰ to 12.77‰. At 700 m, δ¹³C values in both litter (-37.29 ± 1.09‰) and subsoil (− 34.14 ± 0.46‰) were significantly less negative than at other elevations, indicating stronger ¹³C enrichment ( p < 0.05; Table 2 ). Table 2 SOC and δ¹³C characteristics in soil and apoplastic layers at various elevations. Mean organic litter(%) Mean litter δ 13 C(‰) Mean SOC (%) Mean Soil δ 13 C(‰) Δδ 13 C(‰) 700 45.67 ± 2.61 -37.29 ± 1.09 32.22 a -34.14 ± 0.46 bc 2.85 ± 0.96 1000 46.68 ± 1.39 -39.10 ± 0.59 4.95 c -27.48 ± 0.69 a 12.67 ± 0.92 1300 49.02 ± 0.64 -38.57 ± 1.51 3.72 c -27.82 ± 0.48 a 12.77 ± 1.96 1514 48.01 ± 1.91 -37.80 ± 0.75 11.09 bc -29.23 ± 0.67 ab 10.04 ± 1.85 1687 48.14 ± 1.19 -39.82 ± 1.33 14.11 b -33 ± 0.61 bc 6.85 ± 1.46 1818 49.37 ± 0.55 -40.35 ± 0.39 9.86 bc -30.84 ± 0.47 ab 10.54 ± 0.99 Sampling locations with mean SOC and soil and litter δ 13 C values (0–15 cm and 0–60 cm, respectively). Mean ± standard deviation (SD) is indicated by the values. a, b, c Groups with different superscript letters are significantly different (Tukey’s HSD). The strength of the linear relationship between δ¹³C and log(SOC) varies significantly across elevations (R = 0.48–0.93; Table 3 ). The inclusion of litter layers significantly increased β slopes (7.43 ± 0.43 vs. 5.18 ± 0.33; p < 0.0001), improving model fit (R = 0.69–0.91; p < 0.002). This disparity was especially evident at 700, 1300, and 1818 m, indicating that litter-derived carbon plays a dominant role in surface SOC dynamics at these elevations. Table 3 Regression model parameters between log(SOC) and soil δ 13 C value. altitude(m) soil Litter + soil |β| R p value |β| R p value 700 12.93 R = 0.48 p = 0.07 18.44 R = 0.69 p = 0.002 1000 3.75 R = 0.78 p <0.0001 6.04 R = 0.86 p <0.0001 1300 2.42 R = 0.56 p = 0.031 6.08 R = 0.84 p <0.0001 1514 5.11 R = 0.93 p <0.0001 7.73 R = 0.91 p <0.0001 1687 4.05 R = 0.87 p <0.0001 5.59 R = 0.83 p <0.0001 1818 2.42 R = 0.52 p = 0.085 7.75 R = 0.83 p <0.0001 Mean 5.18 ± 0.33 R = 0.87 p <0.0001 7.43 ± 0.43 R = 0.86 p <0.0001 The regression model's slope is shown by the β coefficient, its goodness-of-fit to the data is evaluated using R, and statistical significance testing is performed using the p-value. 3.2. Climate variables affecting soil δ 13 C and β value β values exhibit a weak but statistically significant positive correlation with MAT (R² = 0.28, p = 0.021, slope = 0.926), reaching their maximum at the warmest site (700 m, MAT = 3.2°C, β = 17.96 ± 1.24). A modest negative correlation was also observed between β values and MAP (R² = 0.24, p = 0.033, slope = − 0.007; Fig. 3 b). Soil δ¹³C shows a marginally significant negative correlation with MAP (R² = 0.29, p = 0.067, slope = − 2.05E–5; Fig. 3 d), and a moderate negative correlation MAT (R² = 0.39, p = 0.018, slope = − 0.319; Fig. 3 c). In contrast, δ¹³C values in the litter layer show no significant correlation with climatic variables (p > 0.10). The relationship between temperature and soil δ¹³C follows a unimodal pattern, peaking at mid-elevation (1000 m a.s.l., MAT = 0.44°C, MAP = 389.91 mm) and declining under warmer conditions. 3.3. Edaphic variables affecting soil δ 13 C and β value Across soil profiles (0–60/15 cm), pedogenic factors showed significant vertical and altitudinal stratification. δ¹³C is positively correlated with total potassium (TK: R² = 0.82, p < 0.0001) and a strong negative correlation with total nitrogen (TN: R² = 0.75, p < 0.0001), total phosphorus (TP: R² = 0.68, p < 0.0001), and the C/N ratio (R² = 0.54, p < 0.0001) (Fig. 4 b, c, d, e). Stepwise regression identifies TN, TK, and the C/N ratio as the main predictors of δ¹³C variability that took multicollinearity into account (R² = 0.76, p = 0.006). The β values showed divergent pedogenic controls as they decreased with TK (R² = 0.35, p = 0.006) but increased with TN (R² = 0.64, p < 0.0001) and TP (R² = 0.66, p < 0.0001) (Fig. 4 g, h, i). Neither pH nor the C/N ratio shows significant correlations with β values; a multivariate model that included TN-TP-TK interactions explained 86% of the variance in β values ( p = 0.001) (Fig. 4 f, j). 3.4. Principal component analysis of environmental controls PCA reveals distinct altitudinal controls on soil carbon dynamics.. Edaphic factors (TN, TK, δ¹³C) mainly contributed to PC1 (48.7%), while climatic variables (MAT, MAP) dominated PC2 (26.8%). Together, these two components explained 75.5% of the total variance. As major contributors to PC1, TN (-0.41), TK (-0.42), and TP (-0.37) showed how nitrogen, potassium, and phosphorus work together to affect carbon dynamics. Owing to its unique carbon dynamics under prolonged saturation, the 700 m site showed a noticeable variation along PC1. Samples from high elevations (1,514–1,818 m) cluster in the negative PC2 space (Fig. 5 ), where climate variables such as MAT and MAP were the primary contributors.(Fig. 5 ). 4. Discussion 4.1. Influence of soil depth on δ¹³C and SOC dynamics The vertical variation of SOC and δ¹³C across the elevation gradient is first assessed to determine depth-related dynamics. Soil δ¹³C primarily reflects microbial decomposition and plant-derived inputs (Bhattacharyya et al. 2022 ; Keeling et al. 2017 ). PCA results showed that the 700 m site was separated along the PC1 axis (Fig. 5 ), likely due to its distinct edaphic profile, including high SOC, elevated TK, and atypical δ¹³C values. This pattern may reflect the influence of prolonged waterlogging at this elevation (Table 1), which could suppress aerobic decomposition and promote selective loss of ¹²C(Deroo et al. 2021 ; Gao et al. 2015 ; Minick et al. 2019 ). Such conditions are known to promote ¹³C enrichment in residual SOC (Δδ¹³C = 2.85‰, Table 2 ). However, in the absence of direct measurements of redox potential or microbial activity, this interpretation remains tentative and warrants further investigation. Higher elevations (e.g., 1,687 m) showed suppressed decomposition and lower β-values, reflecting cold-temperature constraints along the PC1 axis (Jiang et al. 2024 ; Kohl et al. 2015 ). Recent carbon inputs from plant litter are typically reflected in topsoil δ¹³C values (Andriollo et al. 2017 ). In C₃ plants, δ¹³C values typically range from − 22‰ to − 32‰, primarily determined by their photosynthetic pathway (Tan et al. 2009 ). The dominance of C3 vegetation throughout the study area is confirmed by our foliar δ¹³C readings (-25‰ to -27‰ degrees; Table 1). Current litter imports are directly reflected in topsoil δ¹³C levels, which indicate that this photosynthetic signature spreads into surface soil layers. However, variations in δ¹³C and SOC content cannot be solely attributed to soil depth. Climatic factors—especially temperature and precipitation—are equally influential in modulating carbon turnover along altitudinal gradients (Sierra Cornejo et al. 2021 ; Zhao et al. 2021 ). Interestingly, the δ¹³C values of litter layers (–37.3‰ to − 40.4‰; Table 2 ) were substantially more negative than the expected range for C₃ vegetation(Tan et al. 2009 ), and even more negative than the foliar δ¹³C measured in this study (–25‰ to − 27‰). Several mechanisms may explain this discrepancy. First, early-stage microbial decomposition may preferentially degrade ¹³C-enriched compounds, leaving behind more ¹²C-rich residues in the litter layer(Guillaume et al. 2015 ; Krüger et al. 2024 ). Second, under moist and potentially anoxic conditions, microbial assimilation can discriminate against ¹³C, further depleting δ¹³C in the remaining organic matter (Blaser and Conrad 2016 ). Third, leaching of dissolved organic carbon (DOC) from surface litter under prolonged waterlogging may selectively remove ¹³C-enriched fractions, enhancing ¹²C accumulation (Fröberg et al. 2007 ; Kammer and Hagedorn 2011 ). 4.2. Climate variables and their role in carbon cycling Although MAT and MAP exhibit statistically significant relationships with β values, their explanatory power is relatively limited (R² = 0.24–0.28), indicating that climate alone cannot account for the observed variation in SOM turnover. This limitation is further illustrated by the convergence of β values in the absence of litter inputs (Table 3 ), suggesting that climatic effects may be indirect, mediated by surface organic inputs or other site-specific processes(Feyissa et al. 2023 ; Wan and He 2020 ). Substantial SOC accumulation at 700 m reinforces the limited role of climate in regulating turnover at this site. While this elevation corresponds to the “optimal temperature niche” for forest productivity (Huang et al. 2019 ), our PCA results (Fig. 5 ) show that soil nutrients (TN, TP, TK) were more strongly associated with SOC turnover than climatic drivers. These findings suggest that climatic factors play a secondary role and are likely modulated by site-specific edaphic and hydrological conditions(Deng et al. 2018 ; Luo et al. 2021 ). In wetlands, stable moisture supply reduces stomatal responsiveness, allowing the direct effect of temperature on carbon assimilation processes to dominate δ¹³C variations. In wetlands, hydrologic controls often override temperature effects, complicating δ¹³C interpretation (Chunli et al. 2020 ). Although foliar δ¹³C decreased as MAP increased, this connection was not statistically significant ( p > 0.05). Reduced rates of decomposition are frequently linked to increased precipitation, which could make forest soils carbon sinks (Carnicer et al. 2019 ; Chang et al. 2024 ; Meier and Leuschner 2010 ; Sulman et al. 2014 ). Soil δ¹³C fluctuations result from complex interactions among climate, vegetation, and elevation. As a result, disentangling the respective contributions of temperature and precipitation is intrinsically difficult. Nevertheless, our data suggest that distinct turnover mechanisms dominate at different elevations: rapid SOC turnover at mid-elevation is likely driven by favorable thermal and nutrient conditions, whereas high-elevation zones stabilize carbon through cold-induced microbial suppression. Increasing elevation induces hydrothermal gradients that directly regulate microbial decomposition and DOC transport efficiency by lowering temperatures and increasing precipitation (Fig. 6 a). Vertical DOC transport through soil solutions amplifies δ¹³C depth gradients (Fig. 6 b) by introducing 13C-enriched organic molecules into subsurface layers (Nakanishi et al. 2012 ). Steep slopes at 1818 m enhanced SOM turnover through DOC runoff losses, demonstrating topography-driven decoupling of decomposition processes from climate influences(Cheng et al. 2010 ), despite altitudinal reductions in microbial diversity (Kang et al. 2023 ). The δ¹³C anomaly may result from the preferential loss of ¹²C, leading to a relative enrichment of ¹³C in the remaining organic matter (Stergiadi et al. 2016 ). The significant negative correlation between MAP at 700 m and δ¹³C (r = -0.39, p =0.018; Fig. 3 ) may be related to DOC leaching as a result of the region's prolonged flooding. This is in line with the 700 m sample sites' independent distribution in the PCA map (Fig. 5 ), indicating that hydrologic conditions may obscure a direct role of climatic factors (Ditzel et al. 2024 ). These patterns were also reflected in β values, which were higher when litter layers were included (7.43 ± 0.43) compared to soils alone (5.18 ± 0.33; Table 3 ). Precipitation had a negative correlation with β-value (R²=0.24; Fig. 3 ), whereas temperature had a positive correlation (R²=0.28) with β-value. This implies that low temperatures at high elevations (> 1,500 m) enhance carbon stability by limiting decomposition, whereas warmer conditions at low to mid elevations (700–1,300 m) promote more rapid SOC turnover (Fig. 6 b). The covariation of temperature and precipitation along the elevation gradient could be the cause of this. While climate explains part of the observed variation, it does not fully account for the spatial heterogeneity in SOC turnover. Soil nutrient availability and composition play a more dominant role, as indicated by both regression and PCA analyses. Our results indicate that 700 m represents a hydrologically distinct zone where anaerobic soil conditions override climatic controls on carbon turnover. 4.3. Edaphic factors affecting SOC and δ¹³C variation There was no discernible relationship between soil pH and δ¹³C in our investigation (Fig. 4 a). Although pH shows no significant correlation, other edaphic factors, particularly the C/N ratio, exhibit stronger associations with isotopic composition and SOM turnover. This may be due to the highly saturated wetland soils, which obscure the effect of pH on δ¹³C. Higher rates of SOC decomposition are indicated by lower C: N ratios (Guillaume et al. 2015 ; Paul 2016 ; Wang et al. 2017 ). This process enhances CO₂ emissions and results in ¹³C enrichment of the remaining soil organic matter (Jiang et al. 2013 ). Although no clear relationship is observed between soil C/N ratio and β values, larger C: N ratios were linked to higher β-values (Fig. 4 j). PC1 (48.7% of variance) is primarily associated with soil nutrient variables, such as TN, TP, TK, and δ¹³C (Fig. 5 ), indicating that nutrient status was a key differentiating factor across sites. PC2 (26.8% variance) showed a negative correlation with MAP (− 0.55) and a positive correlation with MAT (0.54), suggesting that decomposition is reduced in cold, humid high-altitude environments (Fig. 5 ). These patterns are consistent with the β-value trends that were discovered, which showed that mid-altitude sites (700–1,300 m) have the best circumstances for carbon turnover mediated by nutrients. Our results show that β-values and soil total phosphorus (TP) are positively correlated. Prior research has demonstrated that soil microbial communities and soil TP concentration are considerably and favorably correlated, and that microbial composition is influenced by nitrogen (N) inputs. N input has a greater impact on microbial communities than P input, which is consistent with our findings (Fig. 4 b-c,j-h)(van der Bom et al. 2018 ; Wang et al. 2018 ). Elevated levels of total potassium (TK) promote microbial breakdown, which makes it easier for microorganisms to preferentially use and release ¹²C. This enriches ¹³C in leftover organic matter and raises δ¹³C values (Darunsontaya et al. 2012 ). Moreover, potassium (K) increases root exudation and plant development (Qiu et al. 2018 ), adding more organic matter rich in ¹²C to the soil, which impacts SOC breakdown and lowers β-values (Liu et al. 2023 ). This highlights a potential nutrient-driven mechanism of carbon turnover in mid-elevation wetlands. ​ The outlier status of the 700 m site along PC1 likely reflects the previously described waterlogging-induced alterations in nutrient dynamics and carbon stabilization (Darunsontaya et al. 2012 ; Krüger et al. 2024 ). When analyzing altitudinal carbon dynamics, this unique situation emphasizes the importance of taking regional hydrological restrictions into account. The cumulative load of soil factors on PC1 (δ¹³C: 0.39, TN: -0.41, TK: 0.42) is substantially greater than that of climate factors (PC2 load: MAT: 0.54, MAP: -0.55). This suggests that soil nutrient dynamics regulation on carbon isotope fractionation and SOM turnover is more significant than climate driving. The combined effect of elevated TK and TN at 700 m may further explain the site’s distinctive carbon dynamics. These findings underscore the need to integrate site-specific soil and hydrological conditions into ecosystem management strategies. The implications of altitudinal SOC patterns extend beyond theoretical understanding, offering actionable insights for wetland conservation. 4.4. Implications for ecosystem management and future research These findings inform ecosystem management. Research shows that SOC pools at mid-altitudes (700–1,300 m) are more stable, with a maximum β value of 7.43, highlighting the critical role of this zone in carbon sequestration. Priority should be given to preserving native wetlands within this altitudinal range by minimizing disturbances such as drainage and logging, and by restoring hydrological connectivity in degraded sites, such as by rebuilding surface runoff buffer zones (Liu et al. 2024 ). In addition to permafrost studies (Schuur et al. 2015 ), high-altitude regions (> 1,500 m) show the potential for SOC accumulation because low temperatures inhibit decomposition (Bian et al. 2020 ). This underscores the importance of monitoring potential permafrost thaw and associated carbon release under climate change. Despite certain limitations, this study provides valuable insights into the influence of elevation gradients on SOC turnover in forested wetlands. First, the specificity of local hydrothermal conditions may constrain the generalizability of our findings; it remains unclear whether the observed carbon turnover patterns in the CBS forested wetlands apply to other wetland types, such as arid or tropical systems. Second, although the anomalous carbon dynamics observed at 700 m (i.e., high SOC content and enriched δ¹³C) were attributed to waterlogging-induced suppression of aerobic decomposition, this explanation remains inferential. The lack of direct measurements of microbial activity, hydrological fluxes, and redox conditions limits the mechanistic understanding of this hypothesis. Therefore, the proposed waterlogging–carbon preservation mechanism should be viewed as a working hypothesis that warrants further investigation. Third, the ecological interpretation of the β-value as a proxy for SOC turnover also requires further refinement and mechanistic validation. In particular, additional research is needed to elucidate how isotope fractionation, potentially mediated by microbial metabolism and enzymatic activity, affects β-value variation across soil depths and elevations. To better understand the heterogeneity of carbon cycling along elevational gradients and its underlying drivers, future research should prioritize cross-ecosystem comparisons, in situ microbial characterization, and isotope tracer experiments. 5. Conclusions A distinct altitudinal pattern of SOC dynamics in CBS's wooded wetlands was discovered by the study. SOC content decreased with depth, whereas δ¹³C values increased. β-value analysis reveals that soil nutrients (TN, TP, and TK) explain 86% of the variation in organic matter turnover, greatly surpassing the contribution of climatic factors. Temperature peaked δ¹³C at mid-altitude. peaking at intermediate elevation (1000 m). PCA results confirmed that meteorological variables (MAT, MAP) played a secondary role, with soil parameters (TN, TK, TP) emerging as primary drivers of carbon dynamics. An outlier in the PCA space, abnormal carbon dynamics were caused by chronic floods at the 700 m sample site. The mid-altitude region (1,000–1,300 m) exhibited the highest carbon turnover efficiency, whereas the high-altitude zone (> 1,500 m) functioned as a cold-induced carbon stabilization area. Future research should focus on vertical sensor networks to predict carbon-climate feedbacks and microbial metabolic analyses (e.g., PLFA) to assess anaerobic breakdown at 700 m. While monitoring the risk of permafrost thaw at elevations above 1,500 m, conservation efforts should prioritize hydrological restoration in mid-elevation zones (700–1,300 m). Declarations Acknowledgments We would like to thank all the people who participated in the data collection. Concurrently, the authors are grateful to the regional editors and anonymous reviewers whose invaluable insights have played a pivotal role in enhancing the quality of this manuscript. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research was supported by the National Natural Science Foundation of China (NSFC) Joint Fund Project (No. U24A202301). Authors' contributions The study's conception and design were collaboratively developed by all authors. Tianjiao Wang was responsible for field investigation and data collection, Mengfei Dou conducted experimental execution and laboratory analysis, Cui Zhang managed sample preparation and data management, and Kun Zhang handled methodology, formal analysis, and visualization. Kun Zhang also served as the lead writer for the initial manuscript draft, with Tingting Wu providing critical revisions and editing. Additionally, Tingting Wu and Weihong Zhu managed project administration, secured funding, and provided supervision.All authors participated in manuscript revisions, granted final approval, and accepted accountability for the work. References Andriollo D D, Redin C G, Reichert J M, da Silva L S (2017) Soil carbon isotope ratios in forest-grassland toposequences to identify vegetation changes in southern Brazilian grasslands. CATENA 159: 126-135. https://doi.org/10.1016/j.catena.2017.08.012 Angst G, Mueller K E, Eissenstat D M, Trumbore S, Freeman K H et al (2019) Soil organic carbon stability in forests: Distinct effects of tree species identity and traits. 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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-6852862","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500020106,"identity":"9eab7c92-3ed7-46b9-9227-d8ae4b7c4180","order_by":0,"name":"Kun Zhang","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Zhang","suffix":""},{"id":500020110,"identity":"b6a41c68-474d-4b5e-9845-f5f1bb232e7a","order_by":1,"name":"Tingting Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYLCCDwwHQJQBseqZGRtnkKylmYckLQY38o8/tm27k9jA3rxNgqHmDjFakhmbc9ueJTbwHCuTYDj2jLAWM4iWw4kNEjlmEowNh4nUYgnSIv+GFC2MYFt4iNRif+ax4cyec8+M23jSii0SjhGhRbI98cGHH2V3ZPvZD2+88aGGCC1gwMjGwMAGYiQQqQEI/hCvdBSMglEwCkYgAAAUbT1N76VfNAAAAABJRU5ErkJggg==","orcid":"","institution":"Yanbian University","correspondingAuthor":true,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Wu","suffix":""},{"id":500020112,"identity":"9e311aba-3de8-4aa0-a859-2ea6df37d7e7","order_by":2,"name":"Tianjiao Wang","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Tianjiao","middleName":"","lastName":"Wang","suffix":""},{"id":500020120,"identity":"a39121a4-5be0-44ee-9642-2bc1e2e3ce0f","order_by":3,"name":"Mengfei Dou","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Mengfei","middleName":"","lastName":"Dou","suffix":""},{"id":500020123,"identity":"a3eaeefc-929f-4392-8cde-fe66c5a2fc7e","order_by":4,"name":"Cui Zhang","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Zhang","suffix":""},{"id":500020126,"identity":"45bdbe48-4062-4cc8-ab2b-31ae6329c19b","order_by":5,"name":"Weihong Zhu","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-06-09 09:24:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6852862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6852862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89106115,"identity":"f3ec25aa-8237-4dfb-a9dc-d61a4922fd24","added_by":"auto","created_at":"2025-08-14 17:31:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346144,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of research locations for forest wetlands on CBS's north slope. The land cover map dataset used in this investigation was given by the Chinese Academy of Sciences' Data Center for Resources and Environmental Sciences (http://www.resdc.cn).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/26a3fba5273349ea0a596bb8.png"},{"id":89106108,"identity":"ed9f86c7-dc26-4865-ba24-0e9d91a3c66b","added_by":"auto","created_at":"2025-08-14 17:31:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b \u003c/strong\u003eFeatures of the vertical distribution of δ¹³C values and SOC content at various altitude gradients.\u003cstrong\u003e \u003c/strong\u003eThe change in SOC with elevation and soil depth is depicted in Fig. a. The variations in δ¹³C values that correspond to various heights and soil depths are simultaneously shown in Fig. b. Elevations are represented by lines of varying colors.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/9c8551af5041883e045bfba3.png"},{"id":89106828,"identity":"819927d9-1167-4cb1-b3f7-c98d8ba8b3d8","added_by":"auto","created_at":"2025-08-14 17:47:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182030,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-d \u003c/strong\u003ePlant litter layer δ\u003csup\u003e13\u003c/sup\u003eC and β values along gradients of temperature (a, c) and precipitation (b, d). MAP and MAT refer to mean annual precipitation and mean annual temperature, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/b9d16df09467a4d7e31cf4cc.png"},{"id":89106703,"identity":"7d067c97-e2da-4859-9ee7-e6bf03219fb6","added_by":"auto","created_at":"2025-08-14 17:39:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-j \u003c/strong\u003ePhysical and chemical characteristics of soil are coupled with δ¹³C and β values. Connections between edaphic factors (soil pH (a, f), TN (b, g), TP (c, h), TK (d, i), and C/N (e, j)) and soil δ\u003csup\u003e13\u003c/sup\u003eC and β value.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/46bc5ab0ebed5220b1f41510.png"},{"id":89107484,"identity":"8abf1e95-02cb-4bc0-8fc5-eea7a52eebeb","added_by":"auto","created_at":"2025-08-14 17:55:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106096,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of environmental determinants using Principal Component Analysis (PCA). The distribution of forest wetland environmental factors at various elevations on PC1 (48.7%) and PC2 (26.8%) is depicted in the figure (cumulative variance contribution 75.5%).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/61c7c531b1c8c2ecd32e1109.png"},{"id":89106113,"identity":"b56f9108-e849-4c6d-b247-67eca311bffa","added_by":"auto","created_at":"2025-08-14 17:31:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":401072,"visible":true,"origin":"","legend":"\u003cp\u003eThe dual-driver mechanism of carbon turnover under altitudinal gradients: a conceptual model. The ecological differentiation pattern of the altitudinal gradient samples of the wetland forest on the north slope of the CBS is explained by Figure A, which displays the dominant tree species composition and carbon dynamics partitioning characteristics of the six altitudinal gradient samples ranging from 700 m to 1818 m. The high elevation zone (1514-1818 m, labeled in blue) forms a low-temperature suppressed zone of carbon stabilization, the low and middle elevation zone on the right (700-1300 m, labeled in red) is a high β-driven zone of rapid organic matter turnover, and the stagnant environment (700 m, labeled in reddish gray) is significantly enriched in δ¹³C due to anaerobic decomposition. MAT and MAP gradient changes are expressed by variations in the diameters of solar and rain clouds. The mechanisms are illustrated in Figure (b) byMAT and MAP gradient changes are expressed by variations in the diameters of solar and rain clouds. The methods via which carbon dynamics are regulated across the altitudinal gradient by climate, soil, and microbial interactions are shown in Figure (b). Positive and negative regulatory links are denoted by red and blue arrows, respectively. Gray arrows point to climate-soil characteristics that are not directly controlled.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/71a70c009d041752ed6c27d4.png"},{"id":92839609,"identity":"39e672ab-6019-435e-ada8-51c78750adf9","added_by":"auto","created_at":"2025-10-06 08:39:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2205405,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6852862/v1/84d65f9e-ecaa-4119-996f-7173e915b7b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altitudinal Variation in Soil Organic Carbon Turnover: Decoupling Climate and Edaphic Drivers in Changbai Mountain Forest Wetlands","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWetlands function as critical carbon sinks, storing approximately 20\u0026ndash;30% of global soil organic carbon (SOC) while occupying only 5\u0026ndash;8% of the Earth's terrestrial surface (Lal \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Forest wetlands\u0026mdash;the largest and most ecologically significant wetland subtype\u0026mdash;act as transitional zones between terrestrial and aquatic ecosystems (Bridgham et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Through the modulation of evapotranspiration and greenhouse gas exchange, forest wetlands serve as critical nodes in the terrestrial carbon fluxes and play a pivotal role in regulating climate at both regional and global scales (Davidson 2022). Despite their importance in carbon sequestration, forest wetlands face unprecedented challenges under global climate change.\u003c/p\u003e\u003cp\u003eClimate change affects wetland carbon cycling through complex interactions among environmental factors (Par\u0026eacute; et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In forested wetlands, rising groundwater levels trigger shifts in plant communities, soil properties, and biodiversity, reshaping carbon cycle dynamics (Mitsch et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Climate change affects organic carbon decomposition and sequestration by altering soil microbial communities (Dong et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Temperature variability and shifting precipitation patterns significantly influence water balance and carbon fluxes in forested wetlands, particularly in regions with pronounced seasonality (Zhao et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the mechanisms by which soil and climatic factors regulate organic carbon transformation along altitudinal gradients in forest wetlands are still poorly understood. Addressing these knowledge gaps is critical for improving terrestrial carbon feedback models and informing effective climate mitigation strategies.\u003c/p\u003e\u003cp\u003eLong-term carbon pool dynamics, characterized by disturbance events, gradual accumulation, and high spatiotemporal variability, remain difficult to quantify using conventional approaches such as destructive sampling. Stable carbon isotope techniques offer a promising alternative. By precisely tracing the dynamics of organic carbon through carbon stable Isotope composition (δ\u0026sup1;\u0026sup3;C) signatures in the plant-soil system, these approaches offer crucial mechanistic insights into carbon cycling processes at the ecosystem scale (McCloskey et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe majority of soil organic matter comes from litter, and vegetation signatures are usually reflected in soil δ\u0026sup1;\u0026sup3;C readings (Bradford et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cotrufo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Environmental variables such as temperature that influence litter δ\u0026sup1;\u0026sup3;C may also affect soil δ\u0026sup1;\u0026sup3;C composition (Cernusak et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hicks Pries et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The relevance of these factors in soil δ\u0026sup1;\u0026sup3;C dynamics is still up for debate, although temperature impacts on plant δ\u0026sup1;\u0026sup3;C are often limited (Jia et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This discrepancy implies that a variety of abiotic factors influences soil δ\u0026sup1;\u0026sup3;C patterns in addition to temperature, such as precipitation regimes (Bradford et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Delgado-Baquerizo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), soil C/N ratios (Wang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), pH gradients (Wang et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yuan and Chen \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and other pedochemical characteristics.\u003c/p\u003e\u003cp\u003eThe β coefficient, derived from the linear regression of log-transformed SOC against δ\u0026sup1;\u0026sup3;C values, serves as a robust proxy for SOM turnover by quantifying isotopic fractionation per unit of carbon loss (Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By analyzing several forest locations, earlier research has shown that β coefficients are effective at indicating the degree of soil organic matter (SOM) decomposition (Garten et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). There is a crucial knowledge vacuum regarding SOM dynamics in forested wetlands, particularly primary forest wetlands, as the majority of recent research has been on terrestrial forests and grasslands (Angst et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chari and Taylor \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChangbai Mountain(CBS) exhibits pronounced vertical zonation due to its distinctive geographical and environmental conditions. The terrain causes increased precipitation, and the environmental gradient is steep, with the temperature decreasing by about 0.65\u0026deg;C for every 100 meters of elevation gain (Cheng et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sigalue et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The peatlands here contribute significantly to the global carbon cycle, with an average carbon accumulation intensity of up to 164.43 tons/km\u0026sup2; and a carbon accumulation rate of 38.96 g/(A m\u0026sup2;) (Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, CBS's forest wetlands have not received enough attention in the field of forest wetland research, which has previously focused mostly on the Greater and Lesser Xing'an Mountains (Gu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun 2010). This study focuses on the ecologically significant yet understudied forest wetlands of CBS to investigate their unique biological mechanisms and carbon cycling dynamics.\u003c/p\u003e\u003cp\u003eThis study aims to: (1) examine the altitudinal patterns of SOC dynamics and δ\u003csup\u003e13\u003c/sup\u003eC fractionation across depth profiles in CBS forest wetlands; (2) identify the main drivers of SOC turnover using principal component analysis of climatic and edaphic factors in conjunction with β-value regression slopes; (3) identify carbon cycling patterns along altitudinal gradients and propose targeted conservation strategies to enhance carbon sequestration and restore hydrological function.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study area and soil sample collection\u003c/h2\u003e\u003cp\u003eLocated in southeastern Jilin Province, China, the CBS Nature Reserve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) spans 196,465 hectares and reaches a maximum elevation of 2,744 meters. This temperate forest ecosystem of global ecological significance is situated near the China\u0026ndash;North Korea border (127\u0026deg;43\u0026prime;\u0026ndash;128\u0026deg;17\u0026prime;E, 41\u0026deg;42\u0026prime;\u0026ndash;42\u0026deg;25\u0026prime;N). The region harbors one of East Asia\u0026rsquo;s most intact forest ecosystems and is recognized as a UNESCO Biosphere Reserve (Fu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The region experiences a continental monsoon climate, with mean annual temperatures ranging from 2\u0026deg;C to 4\u0026deg;C and precipitation increasing from 632 mm in lowlands to 1,154 mm at higher elevations(Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From deciduous broadleaf forests below 500 meters to mixed conifer-broadleaf stands (500\u0026ndash;1,100 meters), coniferous forests (1,100\u0026ndash;1,700 meters), Betula ermanii-dominated subalpine woodlands (1,700\u0026ndash;2,100 meters), and alpine tundra above 2,100 meters, the vegetation exhibits clear vertical zonation (Kang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Vegetation across all elevational zones primarily consists of species utilizing the C₃ photosynthetic pathway, such as the dominant tree genera Larix, Betula, Abies, and Picea, which are typical of temperate forest ecosystems (Tan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The spatial heterogeneity of ecosystems across various elevations is significantly shaped by this prominent vertical zonation of vegetation, which is fueled by the gradient distribution of light, heat, water, and soil elements.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Soil and litter sample collection\u003c/h2\u003e\u003cp\u003eSix ecologically distinct forested wetlands were systematically selected along the northern slope of CBS, covering a broad elevation gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;1 provides a summary of the habitat characteristics, elevation, mean annual temperature, yearly precipitation, and geographic coordinates. At 700 m and 1,000 m elevations, five 10 m \u0026times; 10 m plots were established using an \u0026ldquo;S\u0026rdquo;-shaped layout due to the broad extent and relatively uniform texture of these wetlands. For the remaining four elevations, plots were arranged using the plum blossom method, with three 10 m \u0026times; 10 m plots established per site. Plot numbers were determined based on wetland area and plant diversity. Within each plot, three 1 m \u0026times; 1 m subplots were designated along the diagonal to record vegetation and site characteristics.\u003c/p\u003e\u003cp\u003eAt elevations above 1,300 m, shallower soil profiles necessitated finer depth intervals for sampling. At lower altitudes (700\u0026ndash;1,300 m), deeper stratified sampling was used due to the deeper soils. Soil profiles were separated into five depth intervals between 700 and 1300 meters: 0\u0026ndash;5 cm, 5\u0026ndash;10 cm, 10\u0026ndash;20 cm, 20\u0026ndash;40 cm, and 40\u0026ndash;60 cm. Four depth intervals were used for sampling at higher elevations (1,514\u0026ndash;1,818 m), when soil profiles were shallowed to 15 cm: 0\u0026ndash;2 cm, 2\u0026ndash;5 cm, 5\u0026ndash;10 cm, and 10\u0026ndash;15 cm. A total of 91 soil and 19 litter samples were collected using sterile stainless-steel augers, with rigorous contamination controls including triple-rinsing with deionized water between sites. Samples are processed within 24 hours to preserve isotopic integrity, stored at 4\u0026deg;C during transport, and sealed in argon-flushed polyethylene bags to minimize aerobic oxidation. Before physicochemical evaluation, post-collection procedures included mechanical grinding through 2 mm nylon sieves, air-drying at 25\u0026deg;C for 48 hours, and refrigeration at 4\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable.1\u003c/b\u003e Characteristic study locations on CBS's northern slope along the height gradient.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltitude(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDominant tree species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMAT(℃)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAP(mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.04\u0026deg;N,128.33\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLarix olgensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eAlbizia kalkora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStatic water is present, and the earth is supersaturated.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.18\u0026deg;N,128.16\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLarix olgensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eBetula platyphylla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e389.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThe earth is supersaturated, and there is a creek on one side.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.13\u0026deg;N,128.12\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAbies nephrolepis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003ePicea jezoensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eLarix olgensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e677.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSeasonal wetland\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.10\u0026deg;N,128.09\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAbies nephrolepis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003ePicea jezoensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eLarix olgensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1063.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThere are numerous fallen trees at the site, which is in a valley 200 meters from the highway.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.08\u0026deg;N,128.07\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAbies nephrolepis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003ePicea jezoensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eLarix olgensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eBetula platyphylla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1170.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA creek on the east and west sides of the boardwalk.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.06\u0026deg;N,128.06\u0026deg;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLarix olgensis\u003c/em\u003e,\u003c/p\u003e\u003cp\u003e\u003cem\u003eBetula platyphylla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1250.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThe earth is saturated close to the lake, with water in the center.\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\u003eMAP and MAT refer to mean annual precipitation and mean annual temperature, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Laboratory analyses\u003c/h2\u003e\u003cp\u003eSoil pH was measured using a glass electrode in a 1:2.5 soil-to-distilled-water suspension. A Kjeldahl distillation-autotitration system was used to calculate the total nitrogen (TN). Following digestion with hydrofluoric and perchloric acid, total phosphorus (TP) was determined colorimetrically. The concentration of potassium ions in HCL solution, as measured by atomic absorption spectrophotometry, was transformed into the total potassium (TK) content of the soil. Using elemental analysis flow mass spectrometry (912-0003, LICA, China), SOC and δ\u0026sup1;\u0026sup3;C were measured using elemental analyzer\u0026ndash;isotope ratio mass spectrometry (EA-IRMS), with an analytical precision better than 0.15\u0026permil;. The abundance of δ\u0026sup1;\u0026sup3;C can be calculated using the formula below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{\\text{\u0026delta;}}^{\\text{13}}\\text{C}＝\\frac{{\\text{R}}_{\\text{sample}}}{{\\text{R}}_{\\text{PDB}}}-\\text{1}\\times\\:\\text{1000}\\#\\left(\\text{1}\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere R\u003csub\u003esample\u003c/sub\u003e and R\u003csub\u003ePDB\u003c/sub\u003e are the sample's and standard's 13C/12C ratios, respectively, and δ\u003csup\u003e13\u003c/sup\u003eC is the sample's carbon isotope ratio. When measuring δ\u003csup\u003e13\u003c/sup\u003eC in this manner, the standard deviation is roughly 0.2\u0026permil;.\u003c/p\u003e\u003cp\u003eThe calculation of depth-dependent isotopic enrichment (Δδ\u0026sup1;\u0026sup3;C) was as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere 60 cm for elevations\u0026thinsp;\u0026le;\u0026thinsp;1,300 m and 15 cm for elevations\u0026thinsp;\u0026gt;\u0026thinsp;1,300 m correspond to the deepest layer.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data analyses\u003c/h2\u003e\n \u003cp\u003eClimate data for this study were obtained from the China Meteorological Administration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cma.gov.cn/\u003c/span\u003e\u003c/span\u003e), based on records from nine monitoring stations along the northern slope of CBS. For each soil depth profile, log-transformed SOC concentrations and \u0026delta;\u0026sup1;\u0026sup3;C values were analyzed using stepwise regression, where the regression slope was defined as the \u0026beta; value. One-way analysis of variance (ANOVA) was performed to assess statistical significance, while Pearson correlation was used to evaluate relationships between soil \u0026delta;\u0026sup1;\u0026sup3;C and environmental variables. Principal component analysis (PCA) was conducted to identify the primary environmental drivers of soil carbon dynamics along the altitudinal gradient. Statistical significance was determined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using Origin 2021 and SPSS 26.0.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Soil depth profiles affecting δ\u003csup\u003e13\u003c/sup\u003eCand β value\u003c/h2\u003e\u003cp\u003eSignificant differences in SOC and δ\u0026sup1;\u0026sup3;C values are observed across soil depths and elevations. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, SOC concentrations decrease exponentially with depth. The mean SOC at 700 m (32.22%) was markedly higher than at other elevations (3.72\u0026ndash;14.11%; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating an anomalous pattern of carbon accumulation. Except for 700 m a.s.l., soil δ\u0026sup1;\u0026sup3;C showed progressive enrichment with depth at all elevations. Isotopic enrichment is most pronounced in surface soils (0\u0026ndash;10 cm) and stabilizes in deeper layers (15\u0026ndash;60 cm; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The effect of soil depth diminishes the δ\u0026sup1;\u0026sup3;C enrichment with increasing depth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt 1000 m (-27.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u0026permil;) and 1300 m (-27.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u0026permil;), the mean δ\u0026sup1;\u0026sup3;C values were significantly higher than those at other elevations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but there was no significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between them. Litter-layer SOC (45.67\u0026ndash;49.37%) was significantly different from soils, particularly at 700 m, where mean SOC was 18.11\u0026ndash;28.5% higher than at other sites (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Elevational variation may affect decomposition rates and promote carbon sequestration through root-derived inputs. The range of the Δδ\u0026sup1;\u0026sup3;C enrichment was 2.85\u0026permil; to 12.77\u0026permil;. At 700 m, δ\u0026sup1;\u0026sup3;C values in both litter (-37.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u0026permil;) and subsoil (\u0026minus;\u0026thinsp;34.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u0026permil;) were significantly less negative than at other elevations, indicating stronger \u0026sup1;\u0026sup3;C enrichment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSOC and δ\u0026sup1;\u0026sup3;C characteristics in soil and apoplastic layers at various elevations.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean organic litter(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean litter δ\u003csup\u003e13\u003c/sup\u003eC(\u0026permil;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean SOC (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean Soil δ\u003csup\u003e13\u003c/sup\u003eC(\u0026permil;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eΔδ\u003csup\u003e13\u003c/sup\u003eC(\u0026permil;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e45.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-37.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-34.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e46.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.95\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-27.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e12.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e49.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-38.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.72\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-27.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e12.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e48.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-37.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.09\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-29.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e10.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e48.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-39.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e6.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e49.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-40.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.86\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-30.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e10.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\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\u003eSampling locations with mean SOC and soil and litter δ\u003csup\u003e13\u003c/sup\u003eC values (0\u0026ndash;15 cm and 0\u0026ndash;60 cm, respectively). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) is indicated by the values.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea, b, c\u003c/sup\u003e Groups with different superscript letters are significantly different (Tukey\u0026rsquo;s HSD).\u003c/p\u003e\u003cp\u003eThe strength of the linear relationship between δ\u0026sup1;\u0026sup3;C and log(SOC) varies significantly across elevations (R\u0026thinsp;=\u0026thinsp;0.48\u0026ndash;0.93; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The inclusion of litter layers significantly increased β slopes (7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 vs. 5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), improving model fit (R\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;0.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002). This disparity was especially evident at 700, 1300, and 1818 m, indicating that litter-derived carbon plays a dominant role in surface SOC dynamics at these elevations.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression model parameters between log(SOC) and soil δ\u003csup\u003e13\u003c/sup\u003eC value.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ealtitude(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003esoil\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLitter\u0026thinsp;+\u0026thinsp;soil\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e|β|\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e|β|\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\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 regression model's slope is shown by the β coefficient, its goodness-of-fit to the data is evaluated using R, and statistical significance testing is performed using the p-value.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Climate variables affecting soil δ\u003csup\u003e13\u003c/sup\u003eC and β value\u003c/h2\u003e\u003cp\u003eβ values exhibit a weak but statistically significant positive correlation with MAT (R\u0026sup2; = 0.28, p\u0026thinsp;=\u0026thinsp;0.021, slope\u0026thinsp;=\u0026thinsp;0.926), reaching their maximum at the warmest site (700 m, MAT\u0026thinsp;=\u0026thinsp;3.2\u0026deg;C, β\u0026thinsp;=\u0026thinsp;17.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24). A modest negative correlation was also observed between β values and MAP (R\u0026sup2; = 0.24, p\u0026thinsp;=\u0026thinsp;0.033, slope = \u0026minus;\u0026thinsp;0.007; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Soil δ\u0026sup1;\u0026sup3;C shows a marginally significant negative correlation with MAP (R\u0026sup2; = 0.29, p\u0026thinsp;=\u0026thinsp;0.067, slope = \u0026minus;\u0026thinsp;2.05E\u0026ndash;5; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), and a moderate negative correlation MAT (R\u0026sup2; = 0.39, p\u0026thinsp;=\u0026thinsp;0.018, slope = \u0026minus;\u0026thinsp;0.319; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In contrast, δ\u0026sup1;\u0026sup3;C values in the litter layer show no significant correlation with climatic variables (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). The relationship between temperature and soil δ\u0026sup1;\u0026sup3;C follows a unimodal pattern, peaking at mid-elevation (1000 m a.s.l., MAT\u0026thinsp;=\u0026thinsp;0.44\u0026deg;C, MAP\u0026thinsp;=\u0026thinsp;389.91 mm) and declining under warmer conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Edaphic variables affecting soil δ\u003csup\u003e13\u003c/sup\u003eC and β value\u003c/h2\u003e\u003cp\u003eAcross soil profiles (0\u0026ndash;60/15 cm), pedogenic factors showed significant vertical and altitudinal stratification. δ\u0026sup1;\u0026sup3;C is positively correlated with total potassium (TK: R\u0026sup2; = 0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and a strong negative correlation with total nitrogen (TN: R\u0026sup2; = 0.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), total phosphorus (TP: R\u0026sup2; = 0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the C/N ratio (R\u0026sup2; = 0.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c, d, e). Stepwise regression identifies TN, TK, and the C/N ratio as the main predictors of δ\u0026sup1;\u0026sup3;C variability that took multicollinearity into account (R\u0026sup2; = 0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). The β values showed divergent pedogenic controls as they decreased with TK (R\u0026sup2; = 0.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) but increased with TN (R\u0026sup2; = 0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and TP (R\u0026sup2; = 0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, h, i). Neither pH nor the C/N ratio shows significant correlations with β values; a multivariate model that included TN-TP-TK interactions explained 86% of the variance in β values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, j).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Principal component analysis of environmental controls\u003c/h2\u003e\u003cp\u003ePCA reveals distinct altitudinal controls on soil carbon dynamics.. Edaphic factors (TN, TK, δ\u0026sup1;\u0026sup3;C) mainly contributed to PC1 (48.7%), while climatic variables (MAT, MAP) dominated PC2 (26.8%). Together, these two components explained 75.5% of the total variance. As major contributors to PC1, TN (-0.41), TK (-0.42), and TP (-0.37) showed how nitrogen, potassium, and phosphorus work together to affect carbon dynamics. Owing to its unique carbon dynamics under prolonged saturation, the 700 m site showed a noticeable variation along PC1. Samples from high elevations (1,514\u0026ndash;1,818 m) cluster in the negative PC2 space (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), where climate variables such as MAT and MAP were the primary contributors.(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Influence of soil depth on δ\u0026sup1;\u0026sup3;C and SOC dynamics\u003c/h2\u003e\u003cp\u003eThe vertical variation of SOC and δ\u0026sup1;\u0026sup3;C across the elevation gradient is first assessed to determine depth-related dynamics. Soil δ\u0026sup1;\u0026sup3;C primarily reflects microbial decomposition and plant-derived inputs (Bhattacharyya et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Keeling et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). PCA results showed that the 700 m site was separated along the PC1 axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), likely due to its distinct edaphic profile, including high SOC, elevated TK, and atypical δ\u0026sup1;\u0026sup3;C values. This pattern may reflect the influence of prolonged waterlogging at this elevation (Table\u0026nbsp;1), which could suppress aerobic decomposition and promote selective loss of \u0026sup1;\u0026sup2;C(Deroo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Minick et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such conditions are known to promote \u0026sup1;\u0026sup3;C enrichment in residual SOC (Δδ\u0026sup1;\u0026sup3;C\u0026thinsp;=\u0026thinsp;2.85\u0026permil;, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, in the absence of direct measurements of redox potential or microbial activity, this interpretation remains tentative and warrants further investigation. Higher elevations (e.g., 1,687 m) showed suppressed decomposition and lower β-values, reflecting cold-temperature constraints along the PC1 axis (Jiang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kohl et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent carbon inputs from plant litter are typically reflected in topsoil δ\u0026sup1;\u0026sup3;C values (Andriollo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In C₃ plants, δ\u0026sup1;\u0026sup3;C values typically range from \u0026minus;\u0026thinsp;22\u0026permil; to \u0026minus;\u0026thinsp;32\u0026permil;, primarily determined by their photosynthetic pathway (Tan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The dominance of C3 vegetation throughout the study area is confirmed by our foliar δ\u0026sup1;\u0026sup3;C readings (-25\u0026permil; to -27\u0026permil; degrees; Table\u0026nbsp;1). Current litter imports are directly reflected in topsoil δ\u0026sup1;\u0026sup3;C levels, which indicate that this photosynthetic signature spreads into surface soil layers. However, variations in δ\u0026sup1;\u0026sup3;C and SOC content cannot be solely attributed to soil depth. Climatic factors\u0026mdash;especially temperature and precipitation\u0026mdash;are equally influential in modulating carbon turnover along altitudinal gradients (Sierra Cornejo et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, the δ\u0026sup1;\u0026sup3;C values of litter layers (\u0026ndash;37.3\u0026permil; to \u0026minus;\u0026thinsp;40.4\u0026permil;; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were substantially more negative than the expected range for C₃ vegetation(Tan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and even more negative than the foliar δ\u0026sup1;\u0026sup3;C measured in this study (\u0026ndash;25\u0026permil; to \u0026minus;\u0026thinsp;27\u0026permil;). Several mechanisms may explain this discrepancy. First, early-stage microbial decomposition may preferentially degrade \u0026sup1;\u0026sup3;C-enriched compounds, leaving behind more \u0026sup1;\u0026sup2;C-rich residues in the litter layer(Guillaume et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kr\u0026uuml;ger et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, under moist and potentially anoxic conditions, microbial assimilation can discriminate against \u0026sup1;\u0026sup3;C, further depleting δ\u0026sup1;\u0026sup3;C in the remaining organic matter (Blaser and Conrad \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Third, leaching of dissolved organic carbon (DOC) from surface litter under prolonged waterlogging may selectively remove \u0026sup1;\u0026sup3;C-enriched fractions, enhancing \u0026sup1;\u0026sup2;C accumulation (Fr\u0026ouml;berg et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kammer and Hagedorn \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Climate variables and their role in carbon cycling\u003c/h2\u003e\u003cp\u003eAlthough MAT and MAP exhibit statistically significant relationships with β values, their explanatory power is relatively limited (R\u0026sup2; = 0.24\u0026ndash;0.28), indicating that climate alone cannot account for the observed variation in SOM turnover. This limitation is further illustrated by the convergence of β values in the absence of litter inputs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that climatic effects may be indirect, mediated by surface organic inputs or other site-specific processes(Feyissa et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wan and He \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Substantial SOC accumulation at 700 m reinforces the limited role of climate in regulating turnover at this site. While this elevation corresponds to the \u0026ldquo;optimal temperature niche\u0026rdquo; for forest productivity (Huang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), our PCA results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) show that soil nutrients (TN, TP, TK) were more strongly associated with SOC turnover than climatic drivers. These findings suggest that climatic factors play a secondary role and are likely modulated by site-specific edaphic and hydrological conditions(Deng et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn wetlands, stable moisture supply reduces stomatal responsiveness, allowing the direct effect of temperature on carbon assimilation processes to dominate δ\u0026sup1;\u0026sup3;C variations. In wetlands, hydrologic controls often override temperature effects, complicating δ\u0026sup1;\u0026sup3;C interpretation (Chunli et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although foliar δ\u0026sup1;\u0026sup3;C decreased as MAP increased, this connection was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Reduced rates of decomposition are frequently linked to increased precipitation, which could make forest soils carbon sinks (Carnicer et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Meier and Leuschner \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sulman et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Soil δ\u0026sup1;\u0026sup3;C fluctuations result from complex interactions among climate, vegetation, and elevation. As a result, disentangling the respective contributions of temperature and precipitation is intrinsically difficult.\u003c/p\u003e\u003cp\u003eNevertheless, our data suggest that distinct turnover mechanisms dominate at different elevations: rapid SOC turnover at mid-elevation is likely driven by favorable thermal and nutrient conditions, whereas high-elevation zones stabilize carbon through cold-induced microbial suppression.\u003c/p\u003e\u003cp\u003eIncreasing elevation induces hydrothermal gradients that directly regulate microbial decomposition and DOC transport efficiency by lowering temperatures and increasing precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Vertical DOC transport through soil solutions amplifies δ\u0026sup1;\u0026sup3;C depth gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) by introducing 13C-enriched organic molecules into subsurface layers (Nakanishi et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Steep slopes at 1818 m enhanced SOM turnover through DOC runoff losses, demonstrating topography-driven decoupling of decomposition processes from climate influences(Cheng et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), despite altitudinal reductions in microbial diversity (Kang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The δ\u0026sup1;\u0026sup3;C anomaly may result from the preferential loss of \u0026sup1;\u0026sup2;C, leading to a relative enrichment of \u0026sup1;\u0026sup3;C in the remaining organic matter (Stergiadi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The significant negative correlation between MAP at 700 m and δ\u0026sup1;\u0026sup3;C (r = -0.39, \u003cem\u003ep\u003c/em\u003e=0.018; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) may be related to DOC leaching as a result of the region's prolonged flooding. This is in line with the 700 m sample sites' independent distribution in the PCA map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating that hydrologic conditions may obscure a direct role of climatic factors (Ditzel et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These patterns were also reflected in β values, which were higher when litter layers were included (7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43) compared to soils alone (5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Precipitation had a negative correlation with β-value (R\u0026sup2;=0.24; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), whereas temperature had a positive correlation (R\u0026sup2;=0.28) with β-value. This implies that low temperatures at high elevations (\u0026gt;\u0026thinsp;1,500 m) enhance carbon stability by limiting decomposition, whereas warmer conditions at low to mid elevations (700\u0026ndash;1,300 m) promote more rapid SOC turnover (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The covariation of temperature and precipitation along the elevation gradient could be the cause of this. While climate explains part of the observed variation, it does not fully account for the spatial heterogeneity in SOC turnover. Soil nutrient availability and composition play a more dominant role, as indicated by both regression and PCA analyses. Our results indicate that 700 m represents a hydrologically distinct zone where anaerobic soil conditions override climatic controls on carbon turnover.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Edaphic factors affecting SOC and δ\u0026sup1;\u0026sup3;C variation\u003c/h2\u003e\u003cp\u003eThere was no discernible relationship between soil pH and δ\u0026sup1;\u0026sup3;C in our investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Although pH shows no significant correlation, other edaphic factors, particularly the C/N ratio, exhibit stronger associations with isotopic composition and SOM turnover. This may be due to the highly saturated wetland soils, which obscure the effect of pH on δ\u0026sup1;\u0026sup3;C. Higher rates of SOC decomposition are indicated by lower C: N ratios (Guillaume et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Paul \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This process enhances CO₂ emissions and results in \u0026sup1;\u0026sup3;C enrichment of the remaining soil organic matter (Jiang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although no clear relationship is observed between soil C/N ratio and β values, larger C: N ratios were linked to higher β-values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej).\u003c/p\u003e\u003cp\u003ePC1 (48.7% of variance) is primarily associated with soil nutrient variables, such as TN, TP, TK, and δ\u0026sup1;\u0026sup3;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating that nutrient status was a key differentiating factor across sites. PC2 (26.8% variance) showed a negative correlation with MAP (\u0026minus;\u0026thinsp;0.55) and a positive correlation with MAT (0.54), suggesting that decomposition is reduced in cold, humid high-altitude environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These patterns are consistent with the β-value trends that were discovered, which showed that mid-altitude sites (700\u0026ndash;1,300 m) have the best circumstances for carbon turnover mediated by nutrients.\u003c/p\u003e\u003cp\u003eOur results show that β-values and soil total phosphorus (TP) are positively correlated. Prior research has demonstrated that soil microbial communities and soil TP concentration are considerably and favorably correlated, and that microbial composition is influenced by nitrogen (N) inputs. N input has a greater impact on microbial communities than P input, which is consistent with our findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c,j-h)(van der Bom et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Elevated levels of total potassium (TK) promote microbial breakdown, which makes it easier for microorganisms to preferentially use and release \u0026sup1;\u0026sup2;C. This enriches \u0026sup1;\u0026sup3;C in leftover organic matter and raises δ\u0026sup1;\u0026sup3;C values (Darunsontaya et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, potassium (K) increases root exudation and plant development (Qiu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), adding more organic matter rich in \u0026sup1;\u0026sup2;C to the soil, which impacts SOC breakdown and lowers β-values (Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This highlights a potential nutrient-driven mechanism of carbon turnover in mid-elevation wetlands.\u003c/p\u003e\u003cp\u003e\u003cb\u003e​\u003c/b\u003e The outlier status of the 700 m site along PC1 likely reflects the previously described waterlogging-induced alterations in nutrient dynamics and carbon stabilization (Darunsontaya et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kr\u0026uuml;ger et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When analyzing altitudinal carbon dynamics, this unique situation emphasizes the importance of taking regional hydrological restrictions into account. The cumulative load of soil factors on PC1 (δ\u0026sup1;\u0026sup3;C: 0.39, TN: -0.41, TK: 0.42) is substantially greater than that of climate factors (PC2 load: MAT: 0.54, MAP: -0.55). This suggests that soil nutrient dynamics regulation on carbon isotope fractionation and SOM turnover is more significant than climate driving. The combined effect of elevated TK and TN at 700 m may further explain the site\u0026rsquo;s distinctive carbon dynamics. These findings underscore the need to integrate site-specific soil and hydrological conditions into ecosystem management strategies.\u003c/p\u003e\u003cp\u003eThe implications of altitudinal SOC patterns extend beyond theoretical understanding, offering actionable insights for wetland conservation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Implications for ecosystem management and future research\u003c/h2\u003e\u003cp\u003eThese findings inform ecosystem management. Research shows that SOC pools at mid-altitudes (700\u0026ndash;1,300 m) are more stable, with a maximum β value of 7.43, highlighting the critical role of this zone in carbon sequestration. Priority should be given to preserving native wetlands within this altitudinal range by minimizing disturbances such as drainage and logging, and by restoring hydrological connectivity in degraded sites, such as by rebuilding surface runoff buffer zones (Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition to permafrost studies (Schuur et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), high-altitude regions (\u0026gt;\u0026thinsp;1,500 m) show the potential for SOC accumulation because low temperatures inhibit decomposition (Bian et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This underscores the importance of monitoring potential permafrost thaw and associated carbon release under climate change.\u003c/p\u003e\u003cp\u003eDespite certain limitations, this study provides valuable insights into the influence of elevation gradients on SOC turnover in forested wetlands. First, the specificity of local hydrothermal conditions may constrain the generalizability of our findings; it remains unclear whether the observed carbon turnover patterns in the CBS forested wetlands apply to other wetland types, such as arid or tropical systems. Second, although the anomalous carbon dynamics observed at 700 m (i.e., high SOC content and enriched δ\u0026sup1;\u0026sup3;C) were attributed to waterlogging-induced suppression of aerobic decomposition, this explanation remains inferential. The lack of direct measurements of microbial activity, hydrological fluxes, and redox conditions limits the mechanistic understanding of this hypothesis. Therefore, the proposed waterlogging\u0026ndash;carbon preservation mechanism should be viewed as a working hypothesis that warrants further investigation. Third, the ecological interpretation of the β-value as a proxy for SOC turnover also requires further refinement and mechanistic validation. In particular, additional research is needed to elucidate how isotope fractionation, potentially mediated by microbial metabolism and enzymatic activity, affects β-value variation across soil depths and elevations. To better understand the heterogeneity of carbon cycling along elevational gradients and its underlying drivers, future research should prioritize cross-ecosystem comparisons, in situ microbial characterization, and isotope tracer experiments.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA distinct altitudinal pattern of SOC dynamics in CBS's wooded wetlands was discovered by the study. SOC content decreased with depth, whereas δ\u0026sup1;\u0026sup3;C values increased. β-value analysis reveals that soil nutrients (TN, TP, and TK) explain 86% of the variation in organic matter turnover, greatly surpassing the contribution of climatic factors. Temperature peaked δ\u0026sup1;\u0026sup3;C at mid-altitude. peaking at intermediate elevation (1000 m). PCA results confirmed that meteorological variables (MAT, MAP) played a secondary role, with soil parameters (TN, TK, TP) emerging as primary drivers of carbon dynamics. An outlier in the PCA space, abnormal carbon dynamics were caused by chronic floods at the 700 m sample site. The mid-altitude region (1,000\u0026ndash;1,300 m) exhibited the highest carbon turnover efficiency, whereas the high-altitude zone (\u0026gt;\u0026thinsp;1,500 m) functioned as a cold-induced carbon stabilization area. Future research should focus on vertical sensor networks to predict carbon-climate feedbacks and microbial metabolic analyses (e.g., PLFA) to assess anaerobic breakdown at 700 m. While monitoring the risk of permafrost thaw at elevations above 1,500 m, conservation efforts should prioritize hydrological restoration in mid-elevation zones (700\u0026ndash;1,300 m).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the people who participated in the data collection. Concurrently, the authors are grateful to the regional editors and anonymous reviewers whose invaluable insights have played a pivotal role in enhancing the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (NSFC) Joint Fund Project (No. U24A202301).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s conception and design were collaboratively developed by all authors. Tianjiao Wang was responsible for field investigation and data collection, Mengfei Dou conducted experimental execution and laboratory analysis, Cui Zhang managed sample preparation and data management, and Kun Zhang handled methodology, formal analysis, and visualization. Kun Zhang also served as the lead writer for the initial manuscript draft, with Tingting Wu providing critical revisions and editing. Additionally, Tingting Wu and Weihong Zhu managed project administration, secured funding, and provided supervision.All authors participated in manuscript revisions, granted final approval, and accepted accountability for the work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndriollo D D, Redin C G, Reichert J M, da Silva L S (2017) Soil carbon isotope ratios in forest-grassland toposequences to identify vegetation changes in southern Brazilian grasslands. 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Agric Ecosyst Environ 320: 107591. https://doi.org/10.1016/j.agee.2021.107591 \u003c/li\u003e\n\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stable carbon isotopes, Altitudinal gradient, Soil organic matter turnover, Forest wetlands, Edaphic controls","lastPublishedDoi":"10.21203/rs.3.rs-6852862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6852862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding how soil and climatic factors jointly regulate soil organic carbon (SOC) turnover in forested wetlands is crucial for assessing their role as carbon sources or sinks. This study investigated δ\u0026sup1;\u0026sup3;C-based carbon dynamics along an altitudinal gradient (700\u0026ndash;1,818 m) in the Changbai Mountain to identify key environmental drivers and inform regional carbon management strategies. We analyzed δ\u0026sup1;\u0026sup3;C and β values in soil and litter samples across six elevations, alongside SOC and nutrient concentrations (TN, TP, TK), using principal component analysis to determine the dominant controls on SOC turnover. Results showed that SOC concentrations declined with depth, while δ\u0026sup1;\u0026sup3;C values increased. Notably, the 700 m site exhibited unusually high SOC and δ\u0026sup1;\u0026sup3;C enrichment, likely due to persistent waterlogging. β values peaked at mid-elevations (700\u0026ndash;1,300 m) and were consistently higher in litter than in soil, indicating more active turnover. Edaphic factors accounted for 86% of the variation in β values, far exceeding the influence of climate variables (26.8%). Overall, mid-elevation wetlands exhibited the most favorable conditions for SOC turnover due to optimal moisture and nutrient availability, while high-elevation zones (\u0026gt;\u0026thinsp;1,500 m) functioned as cold-driven carbon sinks. δ\u0026sup1;\u0026sup3;C is demonstrated to be an effective tracer of SOC turnover, underscoring the need for hydrological restoration and nutrient management in mid-altitude wetland ecosystems.\u003c/p\u003e","manuscriptTitle":"Altitudinal Variation in Soil Organic Carbon Turnover: Decoupling Climate and Edaphic Drivers in Changbai Mountain Forest Wetlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 17:31:42","doi":"10.21203/rs.3.rs-6852862/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9838fcee-f981-42de-a1e8-88f8e60d7215","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T08:39:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 17:31:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6852862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6852862","identity":"rs-6852862","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}