Soil pH and moisture drive depth-specific patterns of SOC, TN, and TP along elevational gradients in Chinese montane forests.

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Shihan Fu, Shijie Guo, Jing Wang, Ziqiong Yuan, Xinzhe Wu, Shengyao Mu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7644314/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 Aims Elevational gradients have a strong influence on the spatial distribution of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP). However, the depth-specific mechanisms driving these patterns remain insufficiently understood. This study aims to characterize the altitudinal distribution patterns of SOC, TN, and TP in surface and subsurface soils across mid-elevation mountain forests in China, explore the mechanistic roles of environmental drivers (e.g., pH, soil moisture) in regulating nutrient dynamics, and promote an understanding of C-N-P coupling in montane ecosystems. Methods By synthesizing literature-derived data from 137 mid-altitude transects spanning 22 mountain forest ranges across China, we quantify relationships of SOC, TN, and TP along mid-elevation gradients. Results SOC and TN concentrations exhibited significant positive correlations with elevation in topsoil and subsoil, with more pronounced accumulation trends in subsurface layers, as indicated by 36.84% of samples showing significant increases in SOC and TN concentrations. In contrast, TP displayed heterogeneous patterns, with only 11.1% of surface soils demonstrating significant declines. Our results specifically evaluated the effects of pH and soil moisture on nutrient dynamics across different soil depths, while quantifying the indirect influence of climate. Conclusions By clarifying depth-specific controls on nutrient patterns, our findings highlight the stability of C-N coupling and the central role of pH in stabilizing soil carbon in montane forests, offering theoretical insights for mountain forest management under elevational gradients. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mountain forests account for approximately 20% of the world’s forests and provide important ecosystem services, including climate regulation and biodiversity conservation (Pepin et al., 2015; Antonelli et al., 2018). The uplifted surface related to the diverse microclimate creates localized fertile islands and contributes to high soil heterogeneity (Körner, 2004; Peerigo et al., 2020; Dainese et al., 2024). While high-altitude mountains (> 4000 m, i.e., the Himalayas) have been extensively studied from hydrological and ecological perspectives (Haeberli & Weingartner, 2020), there is a limited understanding of biogeochemical cycles at middle-altitude (< 4000 m) forested mountain areas (Payne et al., 2017; Romeo et al., 2020). The middle-altitude forests provide habitats for more than 1.1 billion people and play a crucial role in influencing ecosystem stability and resilience (Grover, 2014). Soil organic carbon (SOC), nitrogen (TN), and phosphorus (TP) are the essential elements to maintaining soil fertility, sustaining plant productivity, and regulating key biogeochemical cycles (Siedl et al., 2017). Thus, altitudes are critical for regulating soil physicochemical properties and nutrient cycling dynamics. It is generally accepted that the temperature decreases by 0.05 o C with an increase of 100 m in altitude (Tashi et al., 2016 ). Temperature-sensitive biological processes also exhibit linear positive or negative changes in response to altitude gradients, such as changes in plant composition and diversity (Siles & Margesin, 2016 ; Gao et al., 2019 ). Although temperature decline with increasing elevation is widely recognized as a primary driver of SOC accumulation (Luo et al., 2010; Nottingham et al., 2015), SOC and its coupled nutrients do not always change linearly with increasing altitude. Recent studies have highlighted substantial heterogeneity in SOC and TN patterns along altitudinal gradients (Zimmermann et al., 2012; Soong et al., 2021). In contrast, TP exhibits more complex spatial patterns and site-dependent responses to elevation (He et al., 2016 ; Hu et al., 2019 ). Understanding how SOC and associated nutrients respond to coupled or decoupled conditions is critical to better predicting carbon-nutrient feedbacks under ongoing climate change. Altitudinal gradients directly and indirectly modulate environmental variables, including soil physicochemical characteristics, climatic factors (temperature and precipitation), and biological components (microbial communities and vegetation types), collectively shaping the vertical distribution patterns of SOC, TN, and TP (Praeg et al., 2019 ; Jia et al., 2023). As a result of this variability, soil pH and moisture are expected to exert an equally important, if not a greater, role in shaping organic matter accumulation and nutrient cycling than temperature. For instance, decreasing pH with elevation can inhibit microbial activity, thereby enhancing the preservation of organic matter (Rousk et al., 2009). Soil pH exerts dual regulatory effects on soil carbon dynamics. For example, acidic conditions (pH 6.5) accelerate decomposition through organic matter solubilization and microbial metabolism (You et al., 1999 ). Further, changes in soil moisture along elevational gradients also contribute to shaping plant productivity, litter input, and leaching dynamics, thus influencing soil C and N turnover (Davidson & Janssens, 2006; Fierer et al., 2003). Furthermore, soil moisture regulates spatial heterogeneity in redox potential, creating a continuum of aerobic to anaerobic microhabitats that drive divergent organic carbon decomposition pathways, ultimately shaping altitudinal gradients in carbon accumulation (Spivak et al., 2019 ; Miele et al., 2023 ). However, SOC and TN across large spatial scales remain scarce for mountainous forest systems in temperate and subtropical East Asia, where strong monsoonal influences and steep topography create complex eco-climatic gradients. China's extensive mountain ecosystems, characterized by pronounced altitudinal gradients, provide an ideal natural laboratory for investigating the environmental drivers of SOC, TN, and TP cycling. This study focuses on mid-altitude regions (< 4000 m), collecting systematic topsoil and subsoil across 22 mountain forest systems with elevations ranging from 367 to 4200 m in China, covering various climate zones and forest types. Our objectives are to (1) quantify the altitudinal distribution patterns of SOC, TN, TP contents, and their stoichiometric ratios in topsoil and subsoil; (2) clarify the mechanistic roles of pH and soil moisture in regulating SOC, TN, and TP dynamics across soil depths. By examining the covariation between nutrient accumulation and soil physicochemical gradients, we seek to provide a more mechanistic understanding of how mountain ecosystems regulate belowground nutrient cycling beyond temperature-driven expectations. Our findings are important for predicting soil biogeochemical responses to climate warming and altitudinal range shifts in forested mountain systems. Materials and methods Data collection This study searched the literature using the Web of Science ( http://www.webofknowledge.com ) and China National Knowledge Infrastructure ( http://www.cnki.net ). The search keywords were set to "altitude gradient" and "soil physicochemical properties of carbon, nitrogen, phosphorus content," with the search period ending in November 2024. The literature selection followed the above criteria: (1) Each sampling site must have at least four elevation gradients, enabling statistical analysis of the trends in soil C, N, and P changes along the altitude gradient. (2) The elevation span of each sampling site must be greater than 500 meters. (3) Soil sampling depths should include both surface and deep layers relative to each sampling site, specifically categorized for different mountain types into topsoil (≤ 20 cm) and subsoil (> 20 cm). (4) Each soil depth should have at least four sampling points. (5) Altitude gradients influenced by management or subject to anthropogenic disturbances were excluded. A total of 137 altitude gradients were included in this analysis (Fig. 1 and Dataset S1). The geographic information (latitude and longitude), climate (mean annual temperature, MAT, and mean annual precipitation, MAP), dominant species, pH, soil moisture content, and temperature at each altitude gradient are derived from the selected papers or other literature, based on the study locations. If temperature data for the sampling points at each elevation were not available in the literature, we obtained this information through supplementary searches of the papers, cited references, or additional relevant literature. In this study, we investigated 22 mountain forest ranges across distinct geographical regions of China (Fig. 1 , Table S1 ). All studied ecosystems are situated within nature reserves or long-term monitoring stations of ecological research networks. The geographical coordinates of the sampling sites span latitudes from 23.01°N to 44.19°N and longitudes from 82.06°E to 119.39°E. The MAT across sites ranges from − 4°C to 22°C, while the MAP varies between 250 mm and 3487 mm (Fig. 1 ). The altitudes of the studied mountains range from 367 m to 4200 m. The altitude ranges are calculated based on the sampling sites in each mountain, encompassing both the highest and lowest points. Substantial differences in species composition were observed among ecosystems (Supplementary Table 1). Vegetation types along increasing elevation changed from evergreen broadleaf forests to shrublands. Statistical Analysis The linear relationships between log-transformed SOC, TN, TP, and altitude (m) were established to detect altitudinal trends. Principal component analysis (PCA) was used to identify associations between principal components across distinct soil depths, including surface soils (n = 202) and subsurface soils (n = 96). Depth-specific differences in principal component distributions were characterized through biplots overlaid with 95% confidence ellipses under topsoil and subsoil. Multivariate regression analysis was conducted to disentangle the covariation patterns between log-altitude gradients and SOC-TN-TP dynamics about pH and soil moisture. Significant testing (P < 0.05) was employed to evaluate the linear response intensities of altitudinal effects on target attributes. Regression curves with 95% confidence intervals (CIs) were generated to visualize the coupling strengths between altitude-mediated variations in soil pH and moisture, and the SOC-TN-TP system. The gbm package in R was used to quantify the relative contribution rates of depth-dependent influencing factors to the variability of SOC, TN, and TP. The full conceptual framework posited direct climatic regulation (via MAT and MAP) of pH and moisture variation rates, while hypothesizing indirect altitude effects mediated through climatic pathways. All statistical analyses were performed with R software version 4.4.1 ( http://www.r-project.org ). The Chinese altitude dataset is provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) ( http://www.resdc.cn ). Results Variation of SOC, TN, and TP with altitude gradients Overall, the mean value of SOC TN, and TP content across 22 mountains are 81.52 ± 87.06 g/kg, 4.77 ± 3.74g/kg, and 0.74 ± 0.52 g/kg for topsoil and 42.30 ± 41.18g/kg, 3.20 ± 3.42g/kg, and 0.57 ± 0.43g/kg for subsoils (Fig. 1 b, c, d). We found that 84.21% of SOC in topsoil and 57.09% in subsoil exhibited monotonic increasing trends with altitude (Fig. 2 a and b). Specifically, 36.84% of SOC in topsoil showed a significant monotonic increase with altitude (P < 0.05), and 15.79% of the SOC presented a monotonic decrease pattern. The trend of TN was similar to that of SOC, with 36.84% and 15.79% of the TN in topsoil and subsoil showing a significant monotonic increase, 36.84% and 42.11% in topsoil and subsoil exhibiting an insignificant monotonic increase (Fig. 2 c and d). The content of TP showed a larger variation along the altitude gradients. Although 73.68% and 47.37% showed a monotonic increase with increasing altitude, only 21.05% and 15.79% were significant, respectively (Fig. 2 c and d). The elevation variations in C:N, C:P, and N:P ratios in subsoils also exhibited high heterogeneity, with significant and non-significant increases accounting for 10%, 30%, 27.3%, 40%, 30%, and 36.4%, respectively. Driving Factors of SOC, TN, and TP at Different Soil Depths In the PCA analysis, the first principal component accounted for 33.3% of the variance in the original data, and the second principal component accounted for 22.6%. The within-group data for subsurface soils were more compact, with characteristics closely related to the principal components and smaller variability. The within-group data for topsoil exhibited greater variability and covered a wider range of subsoil factors. As a result, the confidence ellipse for subsurface soils was contained within surface soils. For topsoil, the PCA1 explained 35.53% of the variance, and the second principal component explained 21.98%, with a cumulative explained variance of 57.51%. The major factors influencing PCA1, in descending order of influence, were pH, latitude, MAT, MAP, and SOC. For subsurface soils, the PCA1 and PCA2 explained 31.11% and 29.99% of the variance, respectively, with a cumulative explanation of 61.1% (Fig. 3 ). Regulation of Soil pH and Moisture on SOC, TN, and TP at Different Depths Random forest analysis revealed that the soil moisture and pH were significant factors influencing the content of SOC, TN, and TP for both topsoil and subsoils (p < 0.001). In topsoil, the three most important factors for soil organic carbon were mean annual temperature, soil moisture, and latitude. In subsoil, although MAP and MAT caused the greatest decrease in model accuracy for SOC and TN, their influence on TP was secondary to elevation and MAT, following MAP. These three factors were also important for total nitrogen, but their influence varied, with the order being soil moisture > latitude > mean annual temperature. The three most important factors for total phosphorus were latitude, mean annual temperature, and mean annual precipitation (Fig. 4 ). As the pH slope increased in subsurface soils, the slope for soil organic carbon and total nitrogen significantly decreased (P < 0.05); the slope for total phosphorus showed a decreasing trend in both surface and subsurface soils. As the soil moisture slope increased, the slopes for total nitrogen and total phosphorus also showed decreasing trends, though this decrease was insignificant. When the slopes for soil organic carbon and total nitrogen in surface soils increased, they were accompanied by a slight increase in the slopes for soil moisture and pH. This suggests that the changes in soil pH and moisture with elevation are related to the variation of SOC, TN, and TP with elevation (Fig. 5 ). The pH slope had a significantly greater influence on TN and TP than the soil moisture slope. In contrast, the slope of soil moisture had a greater impact on soil organic carbon than the pH slope (Fig. 5 ). Discussion Elevational Distribution Characteristics of Soil Organic Carbon and Total Nitrogen Content Our results demonstrated the monotonic increase in both SOC and TN contents with altitude, consistent with numerous previous studies (Jia et al., 2023; Tashi et al., 2016 ; Yin et al., 2022 ; Zeng et al., 2025 ). This altitude-dependent increase in SOC and TN implies a greater potential for storing SOC and TN in mountain forests, which enhances the proportions of below-ground C and N accumulation. Additionally, the combination of lower temperatures and higher humidity at higher altitudes inhibits the mineralization and decomposition of soil organic matter (Garten & Hanson 2006 ). In contrast, a subset of studies suggests that SOC and TN contents decreased with increasing elevation (He et al. 2022; Xia et al. 2023) or exhibit a hump shape (Li et al. 2017; Zhang et al. 2021b), or even display a bimodal pattern (He et al. 2022), which diverges from our results. Such discrepancies may be linked to the inherent complexity and heterogeneity of different ecosystems. For instance, Wang et al. ( 2004 ) observed fluctuating patterns of SOC and TN contents on the southeastern slope of Gongga Mountain, highlighting a distinct transitional vegetation type between broadleaf and coniferous forests, where plant biomass exceed those of either forest type, thereby significantly contributing to organic matter accumulation. These variations highlight how SOC and TN characteristics vary depending on topography and vegetative conditions. The strong correlation between SOC and TN may be attributed to their mutual association with soil organic matter (Yang & Luo, 2011 ). Organic N accounts for a significant proportion of TN in soils (Xiong et al., 2020 ), which explains the parallel patterns observed between C and N. This close coupling is consistent with findings from the Qilian Mountains (Bai et al., 2024 ) and Tian Shan Mountains (Yu et al., 2023), and is more pronounced in subsoils, likely due to differences in vertical mobility and greater environmental buffering at depth (He et al., 2021). Random forest regression analysis reveals that soil moisture, elevation, and pH significantly influence the reserves of SOC, TN, and total phosphorus (p < 0.001) in topsoil and subsoil (Fig. 4 ). Soil moisture is a crucial environmental factor influencing soil organic carbon content and nitrogen cycling (Li et al., 2020). It plays a vital role in the metabolic processes of plants and microorganisms, often altering the balance between soil carbon and nitrogen inputs and outputs, thus impacting SOC and TN content (Li et al. 2020). As elevation increases, annual precipitation also rises, with higher altitudes experiencing a greater frequency and total rainfall than lower elevations, and a proportionately greater occurrence of misty weather (Pan et al., 2009 ). Elevated humidity not only enhances photosynthetic capacity and plant productivity but also inhibits microbial decomposition of organic matter (Li et al., 2017), promoting SOC and TN accumulation. Soil pH also showed a significant association with SOC and TN, with lower pH values generally linked to higher SOC concentrations (Fig. 5 ). This finding is consistent with previous studies (Hu et al., 2012 ), which have demonstrated that SOC and TN exhibit opposing trends in soil pH. Research indicates that soil pH primarily influences the dynamic changes in soil carbon and nitrogen through chemical and microbial biochemical processes (Zhang et al. 2021a). Mechanistically, increasing pH can increase the leaching of SOC and organic nitrogen by reducing humic colloid binding capacity (Andersson et al., 2000 ; Temminghoff et al., 1997 ). Additionally, pH is a limiting factor affecting microbial activity (Ning et al., 2021 ); thus, a decline in soil pH can diminish microbial activity, weaken the turnover capacity of SOC, and consequently promote its accumulation (Yu et al., 2024 ). Although both soil pH and moisture influence SOC, our results indicate that moisture has a stronger effect on elevational SOC trends than pH. This contrasts with linear regression models and may reflect deeper interactions involving soil structure (Shi et al., 2023 ), microbial composition (Liu et al., 2021 ; Lu et al., 2023 ), and climate-soil feedbacks (Meng et al., 2015 ). Our results showed a dynamic equilibrium or insignificant effects of elevation on temperature and moisture, resulting in a relatively stable SOC content. Additional influences such as radiation, CO₂ concentration, and soil mineral properties at high elevations further shape microbial activity and nutrient cycling (Pan et al., 2009 ; Xiong et al., 2022 ). However, these mechanisms require further investigation to explain the elevation-dependent variability in SOC and TN accumulation fully. Elevational Distribution Characteristics of Total Phosphorus Content in Soil Previous research has not agreed on the variation patterns of TP content and its components in soils at different elevations (De Feudis et al. 2016 ; Hou et al. 2018 ; Lin et al. 2021). The inconsistency of TP content with increasing elevation likely stems from the complex responses of TP to multiple interacting factors of vegetation type, parent material, and geochemical processes (Tao et al., 2008; Vincent et al., 2014; Zhou et al., 2016 ). Our results reveal that 11.1% of topsoil has a significantly decreased TP content with increasing elevation (Fig. 2 ), while 16.7% show a non-significant decline. Conversely, substantial and non-significant increases account for 16.7% and 55.6% of the samples, respectively. The trend of subsoil TP content shows non-significant increases in 60% of the samples, while significant increases and non-significant decreases account for 10%, and significant decreases account for 20%. Even within the same climatic zones and soil types, trends in TP may diverge significantly. For instance, both the Daiyun Mountain and Wuyi Mountain regions are located within the subtropical climate zone of Fujian Province, China, and share similar soil types, including yellow and red soil, yet the elevation–TP relationships in these two regions are contrasting (Lin et al., 2021; Wu et al., 2020). Such disparities may arise from distinct vegetation types and differing physicochemical properties of the soils. Moreover, identical environmental factors may have opposing effects on soil TP content. Under wet conditions, these elements are expected to leach due to precipitation (Hou et al., 2018 ) and can be rapidly absorbed, utilized, and stored by plants (Yu et al., 2018 ). Conclusions In summary, our study revealed the key drivers governing the spatial distribution of SOC, TN, and TP along elevational gradients in montane forests. We further found that soil moisture and pH are important factors in regulating the accumulation of SOC and TN. In contrast, TP content exhibits considerable spatial heterogeneity and lacks a consistent response to elevation. Random forest revealed that soil moisture was a primary factor regulating SOC and TN accumulation, while pH exerted variable but significant effects. This implies that elevational control over nutrient cycling emerges not as a direct response to climatic variation, but is limited by the soil microenvironmental conditions. The results of our study indicate a mismatch between the geological and biological cycles of rock-derived nutrients (TP) and bioaccumulated nutrients (SOC and TN). Therefore, understanding this decoupling is crucial for mitigating the uncertainty in soil biogeochemistry under climate change scenarios in mountainous regions. Declarations Competing Interest Statement : The authors declare no competing financial interests. Author contributions: J.W. conceived the study. S. Fu, S. Guo, and J. Wang conducted the data analysis and drafted the manuscript. Y. Qiao and H. Chai provided critical feedback and revisions. X.W. and S.M. provided technical support on the method. S. Fu, S. Guo, X.W., and S.M. contributed to data collection. All co-authors have made substantial contributions to this work. Acknowledgments: We sincerely thank the hardworking students and researchers who assisted in conducting the field surveys. 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Proceedings of the National Academy of Sciences, 122(16), e2413981122 Zhou J, Wu Y, Bing H, Yang Z, Wang J, Sun H, Sun S, Luo J (2016) Variations in soil phosphorus biogeochemistry across six vegetation types along an altitudinal gradient in SW China. CATENA 142:102–111 Supplementary Files supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7644314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521598688,"identity":"bf8d795a-32be-4806-8345-df9816b75445","order_by":0,"name":"Shihan Fu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shihan","middleName":"","lastName":"Fu","suffix":""},{"id":521598689,"identity":"7c9d1d6b-3636-4d3d-ac4f-d0af3a69ed38","order_by":1,"name":"Shijie Guo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shijie","middleName":"","lastName":"Guo","suffix":""},{"id":521598690,"identity":"1aaa1653-8683-4edf-95d8-961d21b96c87","order_by":2,"name":"Jing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACxmYogx+IP4AEGojWItnAwDiDKC1wYHCAWC3M7byHX/PU3LHbfLv5YDMPg43shgPMzx7gdxhfmjXPsWfJ2+4cSwRqSTPecIDN3AC/Fh4z4xy2w8lmN3LMH/MwHE7ccICHTYKwln+Hk41n5H8E2vKfKC3Gj3PbDtsZSOQA2QwHiLOF+W/f4QSJG2mGjXMMko1nHmYzw6vFsP+M8ccZ3w7b889IftjwpsJOtu948zP8WhoYwM5IbABzQUHFjE89EMgDlYDSiT0BdaNgFIyCUTCSAQAoP0r2RPi6HQAAAABJRU5ErkJggg==","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":521598691,"identity":"e9fc3bfb-86e1-4be5-a135-0c31a6a94a00","order_by":3,"name":"Ziqiong 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12:59:27","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94772,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25035650structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/4da09d147edfe6142777f589.xml"},{"id":93140545,"identity":"332586af-9bdf-455b-9b77-094ab9428966","added_by":"auto","created_at":"2025-10-09 12:59:27","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101734,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/72aa9f894ac7b25fa92954d7.html"},{"id":93141137,"identity":"c2911d35-0ceb-4182-985f-14056b21bbc6","added_by":"auto","created_at":"2025-10-09 13:07:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393396,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the sampling sites for the Chinese mountain forest across the altitudinal gradients. Red and blue dashed lines indicate the topsoil and subsoil, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/e214c28f3a2bcbf603b154df.jpeg"},{"id":93141133,"identity":"e7263b4f-d001-4790-8df3-2fe415088ab9","added_by":"auto","created_at":"2025-10-09 13:07:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":547472,"visible":true,"origin":"","legend":"\u003cp\u003eAltitudinal patterns of log-transformed SOC, TN, and TP across topsoil (0-20 cm, a, c, d) and subsoil (\u0026gt;20 cm, b, d, f). Pie charts with blue and red colors quantify the proportion of significant positive and negative responses (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/ebbe8f688281e3ad8353f5d1.jpeg"},{"id":93141135,"identity":"441f6c2d-6a70-45bc-a6c2-5680fad0ff75","added_by":"auto","created_at":"2025-10-09 13:07:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280035,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between physicochemical properties of topsoil and subsoil with environmental factors (a). MAT and MAP indicate the mean annual air temperature and precipitation.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/98ac58a96ccb2bd98df90451.jpeg"},{"id":93140535,"identity":"61791125-0bed-4d9a-b591-f031703f9788","added_by":"auto","created_at":"2025-10-09 12:59:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":202874,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative importance of abiotic and biotic factors in driving soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in topsoil and subsoil. * , **, and *** represent P-values \u0026lt; 0.05, \u0026lt; 0.01, and \u0026lt; 0.001, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/4c0bb945f7bd4a10d715f6f2.jpeg"},{"id":93140531,"identity":"5e7f76da-08e5-4df4-b987-86b8aea895ea","added_by":"auto","created_at":"2025-10-09 12:59:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221931,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the slopes of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in topsoil and subsoil with the slopes of soil pH and soil moisture. The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) and corrected P value are reported.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/78dab82f77f3bb2fc65a2a25.jpeg"},{"id":96246776,"identity":"c4f68515-36c1-4a75-8ff9-058586c8a3de","added_by":"auto","created_at":"2025-11-19 07:26:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2252815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/65e8e88f-e76d-4615-a7d0-02dff976a1c6.pdf"},{"id":93140547,"identity":"5853efa0-8b02-4d0c-980c-f85f713b23c4","added_by":"auto","created_at":"2025-10-09 12:59:27","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4963630,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644314/v1/f1fb0e88473a83f133c062af.docx"}],"financialInterests":"","formattedTitle":"Soil pH and moisture drive depth-specific patterns of SOC, TN, and TP along elevational gradients in Chinese montane forests.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMountain forests account for approximately 20% of the world\u0026rsquo;s forests and provide important ecosystem services, including climate regulation and biodiversity conservation (Pepin et al., 2015; Antonelli et al., 2018). The uplifted surface related to the diverse microclimate creates localized fertile islands and contributes to high soil heterogeneity (K\u0026ouml;rner, 2004; Peerigo et al., 2020; Dainese et al., 2024). While high-altitude mountains (\u0026gt;\u0026thinsp;4000 m, i.e., the Himalayas) have been extensively studied from hydrological and ecological perspectives (Haeberli \u0026amp; Weingartner, 2020), there is a limited understanding of biogeochemical cycles at middle-altitude (\u0026lt;\u0026thinsp;4000 m) forested mountain areas (Payne et al., 2017; Romeo et al., 2020). The middle-altitude forests provide habitats for more than 1.1\u0026nbsp;billion people and play a crucial role in influencing ecosystem stability and resilience (Grover, 2014).\u003c/p\u003e\u003cp\u003eSoil organic carbon (SOC), nitrogen (TN), and phosphorus (TP) are the essential elements to maintaining soil fertility, sustaining plant productivity, and regulating key biogeochemical cycles (Siedl et al., 2017). Thus, altitudes are critical for regulating soil physicochemical properties and nutrient cycling dynamics. It is generally accepted that the temperature decreases by 0.05 \u003csup\u003eo\u003c/sup\u003eC with an increase of 100 m in altitude (Tashi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Temperature-sensitive biological processes also exhibit linear positive or negative changes in response to altitude gradients, such as changes in plant composition and diversity (Siles \u0026amp; Margesin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although temperature decline with increasing elevation is widely recognized as a primary driver of SOC accumulation (Luo et al., 2010; Nottingham et al., 2015), SOC and its coupled nutrients do not always change linearly with increasing altitude. Recent studies have highlighted substantial heterogeneity in SOC and TN patterns along altitudinal gradients (Zimmermann et al., 2012; Soong et al., 2021). In contrast, TP exhibits more complex spatial patterns and site-dependent responses to elevation (He et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Understanding how SOC and associated nutrients respond to coupled or decoupled conditions is critical to better predicting carbon-nutrient feedbacks under ongoing climate change.\u003c/p\u003e\u003cp\u003eAltitudinal gradients directly and indirectly modulate environmental variables, including soil physicochemical characteristics, climatic factors (temperature and precipitation), and biological components (microbial communities and vegetation types), collectively shaping the vertical distribution patterns of SOC, TN, and TP (Praeg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jia et al., 2023). As a result of this variability, soil pH and moisture are expected to exert an equally important, if not a greater, role in shaping organic matter accumulation and nutrient cycling than temperature. For instance, decreasing pH with elevation can inhibit microbial activity, thereby enhancing the preservation of organic matter (Rousk et al., 2009). Soil pH exerts dual regulatory effects on soil carbon dynamics. For example, acidic conditions (pH\u0026thinsp;\u0026lt;\u0026thinsp;5.5) strengthen the organo-mineral bond to improve stability, whereas neutral to alkaline environments (pH\u0026thinsp;\u0026gt;\u0026thinsp;6.5) accelerate decomposition through organic matter solubilization and microbial metabolism (You et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Further, changes in soil moisture along elevational gradients also contribute to shaping plant productivity, litter input, and leaching dynamics, thus influencing soil C and N turnover (Davidson \u0026amp; Janssens, 2006; Fierer et al., 2003). Furthermore, soil moisture regulates spatial heterogeneity in redox potential, creating a continuum of aerobic to anaerobic microhabitats that drive divergent organic carbon decomposition pathways, ultimately shaping altitudinal gradients in carbon accumulation (Spivak et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Miele et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, SOC and TN across large spatial scales remain scarce for mountainous forest systems in temperate and subtropical East Asia, where strong monsoonal influences and steep topography create complex eco-climatic gradients.\u003c/p\u003e\u003cp\u003eChina's extensive mountain ecosystems, characterized by pronounced altitudinal gradients, provide an ideal natural laboratory for investigating the environmental drivers of SOC, TN, and TP cycling. This study focuses on mid-altitude regions (\u0026lt;\u0026thinsp;4000 m), collecting systematic topsoil and subsoil across 22 mountain forest systems with elevations ranging from 367 to 4200 m in China, covering various climate zones and forest types. Our objectives are to (1) quantify the altitudinal distribution patterns of SOC, TN, TP contents, and their stoichiometric ratios in topsoil and subsoil; (2) clarify the mechanistic roles of pH and soil moisture in regulating SOC, TN, and TP dynamics across soil depths. By examining the covariation between nutrient accumulation and soil physicochemical gradients, we seek to provide a more mechanistic understanding of how mountain ecosystems regulate belowground nutrient cycling beyond temperature-driven expectations. Our findings are important for predicting soil biogeochemical responses to climate warming and altitudinal range shifts in forested mountain systems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eThis study searched the literature using the Web of Science (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.webofknowledge.com\u003c/span\u003e\u003cspan address=\"http://www.webofknowledge.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and China National Knowledge Infrastructure (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cnki.net\u003c/span\u003e\u003cspan address=\"http://www.cnki.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The search keywords were set to \"altitude gradient\" and \"soil physicochemical properties of carbon, nitrogen, phosphorus content,\" with the search period ending in November 2024. The literature selection followed the above criteria: (1) Each sampling site must have at least four elevation gradients, enabling statistical analysis of the trends in soil C, N, and P changes along the altitude gradient. (2) The elevation span of each sampling site must be greater than 500 meters. (3) Soil sampling depths should include both surface and deep layers relative to each sampling site, specifically categorized for different mountain types into topsoil (\u0026le;\u0026thinsp;20 cm) and subsoil (\u0026gt;\u0026thinsp;20 cm). (4) Each soil depth should have at least four sampling points. (5) Altitude gradients influenced by management or subject to anthropogenic disturbances were excluded. A total of 137 altitude gradients were included in this analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Dataset S1). The geographic information (latitude and longitude), climate (mean annual temperature, MAT, and mean annual precipitation, MAP), dominant species, pH, soil moisture content, and temperature at each altitude gradient are derived from the selected papers or other literature, based on the study locations. If temperature data for the sampling points at each elevation were not available in the literature, we obtained this information through supplementary searches of the papers, cited references, or additional relevant literature.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study, we investigated 22 mountain forest ranges across distinct geographical regions of China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All studied ecosystems are situated within nature reserves or long-term monitoring stations of ecological research networks. The geographical coordinates of the sampling sites span latitudes from 23.01\u0026deg;N to 44.19\u0026deg;N and longitudes from 82.06\u0026deg;E to 119.39\u0026deg;E. The MAT across sites ranges from \u0026minus;\u0026thinsp;4\u0026deg;C to 22\u0026deg;C, while the MAP varies between 250 mm and 3487 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The altitudes of the studied mountains range from 367 m to 4200 m. The altitude ranges are calculated based on the sampling sites in each mountain, encompassing both the highest and lowest points. Substantial differences in species composition were observed among ecosystems (Supplementary Table\u0026nbsp;1). Vegetation types along increasing elevation changed from evergreen broadleaf forests to shrublands.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe linear relationships between log-transformed SOC, TN, TP, and altitude (m) were established to detect altitudinal trends. Principal component analysis (PCA) was used to identify associations between principal components across distinct soil depths, including surface soils (n\u0026thinsp;=\u0026thinsp;202) and subsurface soils (n\u0026thinsp;=\u0026thinsp;96). Depth-specific differences in principal component distributions were characterized through biplots overlaid with 95% confidence ellipses under topsoil and subsoil. Multivariate regression analysis was conducted to disentangle the covariation patterns between log-altitude gradients and SOC-TN-TP dynamics about pH and soil moisture. Significant testing (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was employed to evaluate the linear response intensities of altitudinal effects on target attributes. Regression curves with 95% confidence intervals (CIs) were generated to visualize the coupling strengths between altitude-mediated variations in soil pH and moisture, and the SOC-TN-TP system. The \u003cem\u003egbm\u003c/em\u003e package in R was used to quantify the relative contribution rates of depth-dependent influencing factors to the variability of SOC, TN, and TP. The full conceptual framework posited direct climatic regulation (via MAT and MAP) of pH and moisture variation rates, while hypothesizing indirect altitude effects mediated through climatic pathways.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed with R software version 4.4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Chinese altitude dataset is provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.resdc.cn\u003c/span\u003e\u003cspan address=\"http://www.resdc.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eVariation of SOC, TN, and TP with altitude gradients\u003c/h2\u003e\u003cp\u003eOverall, the mean value of SOC TN, and TP content across 22 mountains are 81.52\u0026thinsp;\u0026plusmn;\u0026thinsp;87.06 g/kg, 4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74g/kg, and 0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 g/kg for topsoil and 42.30\u0026thinsp;\u0026plusmn;\u0026thinsp;41.18g/kg, 3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42g/kg, and 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43g/kg for subsoils (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, c, d). We found that 84.21% of SOC in topsoil and 57.09% in subsoil exhibited monotonic increasing trends with altitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b). Specifically, 36.84% of SOC in topsoil showed a significant monotonic increase with altitude (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and 15.79% of the SOC presented a monotonic decrease pattern. The trend of TN was similar to that of SOC, with 36.84% and 15.79% of the TN in topsoil and subsoil showing a significant monotonic increase, 36.84% and 42.11% in topsoil and subsoil exhibiting an insignificant monotonic increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and d). The content of TP showed a larger variation along the altitude gradients. Although 73.68% and 47.37% showed a monotonic increase with increasing altitude, only 21.05% and 15.79% were significant, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and d). The elevation variations in C:N, C:P, and N:P ratios in subsoils also exhibited high heterogeneity, with significant and non-significant increases accounting for 10%, 30%, 27.3%, 40%, 30%, and 36.4%, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDriving Factors of SOC, TN, and TP at Different Soil Depths\u003c/h3\u003e\n\u003cp\u003eIn the PCA analysis, the first principal component accounted for 33.3% of the variance in the original data, and the second principal component accounted for 22.6%. The within-group data for subsurface soils were more compact, with characteristics closely related to the principal components and smaller variability. The within-group data for topsoil exhibited greater variability and covered a wider range of subsoil factors. As a result, the confidence ellipse for subsurface soils was contained within surface soils. For topsoil, the PCA1 explained 35.53% of the variance, and the second principal component explained 21.98%, with a cumulative explained variance of 57.51%. The major factors influencing PCA1, in descending order of influence, were pH, latitude, MAT, MAP, and SOC. For subsurface soils, the PCA1 and PCA2 explained 31.11% and 29.99% of the variance, respectively, with a cumulative explanation of 61.1% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRegulation of Soil pH and Moisture on SOC, TN, and TP at Different Depths\u003c/h2\u003e\u003cp\u003eRandom forest analysis revealed that the soil moisture and pH were significant factors influencing the content of SOC, TN, and TP for both topsoil and subsoils (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In topsoil, the three most important factors for soil organic carbon were mean annual temperature, soil moisture, and latitude. In subsoil, although MAP and MAT caused the greatest decrease in model accuracy for SOC and TN, their influence on TP was secondary to elevation and MAT, following MAP. These three factors were also important for total nitrogen, but their influence varied, with the order being soil moisture\u0026thinsp;\u0026gt;\u0026thinsp;latitude\u0026thinsp;\u0026gt;\u0026thinsp;mean annual temperature. The three most important factors for total phosphorus were latitude, mean annual temperature, and mean annual precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As the pH slope increased in subsurface soils, the slope for soil organic carbon and total nitrogen significantly decreased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the slope for total phosphorus showed a decreasing trend in both surface and subsurface soils. As the soil moisture slope increased, the slopes for total nitrogen and total phosphorus also showed decreasing trends, though this decrease was insignificant. When the slopes for soil organic carbon and total nitrogen in surface soils increased, they were accompanied by a slight increase in the slopes for soil moisture and pH. This suggests that the changes in soil pH and moisture with elevation are related to the variation of SOC, TN, and TP with elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The pH slope had a significantly greater influence on TN and TP than the soil moisture slope. In contrast, the slope of soil moisture had a greater impact on soil organic carbon than the pH slope (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eElevational Distribution Characteristics of Soil Organic Carbon and Total Nitrogen Content\u003c/h2\u003e\u003cp\u003eOur results demonstrated the monotonic increase in both SOC and TN contents with altitude, consistent with numerous previous studies (Jia et al., 2023; Tashi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This altitude-dependent increase in SOC and TN implies a greater potential for storing SOC and TN in mountain forests, which enhances the proportions of below-ground C and N accumulation. Additionally, the combination of lower temperatures and higher humidity at higher altitudes inhibits the mineralization and decomposition of soil organic matter (Garten \u0026amp; Hanson \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, a subset of studies suggests that SOC and TN contents decreased with increasing elevation (He et al. 2022; Xia et al. 2023) or exhibit a hump shape (Li et al. 2017; Zhang et al. 2021b), or even display a bimodal pattern (He et al. 2022), which diverges from our results. Such discrepancies may be linked to the inherent complexity and heterogeneity of different ecosystems. For instance, Wang et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) observed fluctuating patterns of SOC and TN contents on the southeastern slope of Gongga Mountain, highlighting a distinct transitional vegetation type between broadleaf and coniferous forests, where plant biomass exceed those of either forest type, thereby significantly contributing to organic matter accumulation. These variations highlight how SOC and TN characteristics vary depending on topography and vegetative conditions. The strong correlation between SOC and TN may be attributed to their mutual association with soil organic matter (Yang \u0026amp; Luo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Organic N accounts for a significant proportion of TN in soils (Xiong et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which explains the parallel patterns observed between C and N. This close coupling is consistent with findings from the Qilian Mountains (Bai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Tian Shan Mountains (Yu et al., 2023), and is more pronounced in subsoils, likely due to differences in vertical mobility and greater environmental buffering at depth (He et al., 2021).\u003c/p\u003e\u003cp\u003eRandom forest regression analysis reveals that soil moisture, elevation, and pH significantly influence the reserves of SOC, TN, and total phosphorus (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in topsoil and subsoil (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Soil moisture is a crucial environmental factor influencing soil organic carbon content and nitrogen cycling (Li et al., 2020). It plays a vital role in the metabolic processes of plants and microorganisms, often altering the balance between soil carbon and nitrogen inputs and outputs, thus impacting SOC and TN content (Li et al. 2020). As elevation increases, annual precipitation also rises, with higher altitudes experiencing a greater frequency and total rainfall than lower elevations, and a proportionately greater occurrence of misty weather (Pan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Elevated humidity not only enhances photosynthetic capacity and plant productivity but also inhibits microbial decomposition of organic matter (Li et al., 2017), promoting SOC and TN accumulation.\u003c/p\u003e\u003cp\u003eSoil pH also showed a significant association with SOC and TN, with lower pH values generally linked to higher SOC concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding is consistent with previous studies (Hu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which have demonstrated that SOC and TN exhibit opposing trends in soil pH. Research indicates that soil pH primarily influences the dynamic changes in soil carbon and nitrogen through chemical and microbial biochemical processes (Zhang et al. 2021a). Mechanistically, increasing pH can increase the leaching of SOC and organic nitrogen by reducing humic colloid binding capacity (Andersson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Temminghoff et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Additionally, pH is a limiting factor affecting microbial activity (Ning et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); thus, a decline in soil pH can diminish microbial activity, weaken the turnover capacity of SOC, and consequently promote its accumulation (Yu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough both soil pH and moisture influence SOC, our results indicate that moisture has a stronger effect on elevational SOC trends than pH. This contrasts with linear regression models and may reflect deeper interactions involving soil structure (Shi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), microbial composition (Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and climate-soil feedbacks (Meng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our results showed a dynamic equilibrium or insignificant effects of elevation on temperature and moisture, resulting in a relatively stable SOC content. Additional influences such as radiation, CO₂ concentration, and soil mineral properties at high elevations further shape microbial activity and nutrient cycling (Pan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, these mechanisms require further investigation to explain the elevation-dependent variability in SOC and TN accumulation fully.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eElevational Distribution Characteristics of Total Phosphorus Content in Soil\u003c/h2\u003e\u003cp\u003ePrevious research has not agreed on the variation patterns of TP content and its components in soils at different elevations (De Feudis et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hou et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lin et al. 2021). The inconsistency of TP content with increasing elevation likely stems from the complex responses of TP to multiple interacting factors of vegetation type, parent material, and geochemical processes (Tao et al., 2008; Vincent et al., 2014; Zhou et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results reveal that 11.1% of topsoil has a significantly decreased TP content with increasing elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while 16.7% show a non-significant decline. Conversely, substantial and non-significant increases account for 16.7% and 55.6% of the samples, respectively. The trend of subsoil TP content shows non-significant increases in 60% of the samples, while significant increases and non-significant decreases account for 10%, and significant decreases account for 20%.\u003c/p\u003e\u003cp\u003eEven within the same climatic zones and soil types, trends in TP may diverge significantly. For instance, both the Daiyun Mountain and Wuyi Mountain regions are located within the subtropical climate zone of Fujian Province, China, and share similar soil types, including yellow and red soil, yet the elevation\u0026ndash;TP relationships in these two regions are contrasting (Lin et al., 2021; Wu et al., 2020). Such disparities may arise from distinct vegetation types and differing physicochemical properties of the soils. Moreover, identical environmental factors may have opposing effects on soil TP content. Under wet conditions, these elements are expected to leach due to precipitation (Hou et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and can be rapidly absorbed, utilized, and stored by plants (Yu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study revealed the key drivers governing the spatial distribution of SOC, TN, and TP along elevational gradients in montane forests. We further found that soil moisture and pH are important factors in regulating the accumulation of SOC and TN. In contrast, TP content exhibits considerable spatial heterogeneity and lacks a consistent response to elevation. Random forest revealed that soil moisture was a primary factor regulating SOC and TN accumulation, while pH exerted variable but significant effects. This implies that elevational control over nutrient cycling emerges not as a direct response to climatic variation, but is limited by the soil microenvironmental conditions. The results of our study indicate a mismatch between the geological and biological cycles of rock-derived nutrients (TP) and bioaccumulated nutrients (SOC and TN). Therefore, understanding this decoupling is crucial for mitigating the uncertainty in soil biogeochemistry under climate change scenarios in mountainous regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e\u003cb\u003eCompeting Interest Statement\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions:\u003c/h2\u003e\u003cp\u003eJ.W. conceived the study. S. Fu, S. Guo, and J. Wang conducted the data analysis and drafted the manuscript. Y. Qiao and H. Chai provided critical feedback and revisions. X.W. and S.M. provided technical support on the method. S. Fu, S. Guo, X.W., and S.M. contributed to data collection. All co-authors have made substantial contributions to this work.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eWe sincerely thank the hardworking students and researchers who assisted in conducting the field surveys. This work is financially supported by the National Natural Science Foundation of China (42201062, 32201397) and the College Students' Innovative Entrepreneurial Training Program (202410225320).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersson S, Nilsson SI, Saetre P (2000) Leaching of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in mor humus as affected by temperature and pH. 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CATENA 142:102\u0026ndash;111\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7644314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7644314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eAims\u003c/b\u003e\u003c/p\u003e\u003cp\u003eElevational gradients have a strong influence on the spatial distribution of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP). However, the depth-specific mechanisms driving these patterns remain insufficiently understood. This study aims to characterize the altitudinal distribution patterns of SOC, TN, and TP in surface and subsurface soils across mid-elevation mountain forests in China, explore the mechanistic roles of environmental drivers (e.g., pH, soil moisture) in regulating nutrient dynamics, and promote an understanding of C-N-P coupling in montane ecosystems.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBy synthesizing literature-derived data from 137 mid-altitude transects spanning 22 mountain forest ranges across China, we quantify relationships of SOC, TN, and TP along mid-elevation gradients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSOC and TN concentrations exhibited significant positive correlations with elevation in topsoil and subsoil, with more pronounced accumulation trends in subsurface layers, as indicated by 36.84% of samples showing significant increases in SOC and TN concentrations. In contrast, TP displayed heterogeneous patterns, with only 11.1% of surface soils demonstrating significant declines. Our results specifically evaluated the effects of pH and soil moisture on nutrient dynamics across different soil depths, while quantifying the indirect influence of climate.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBy clarifying depth-specific controls on nutrient patterns, our findings highlight the stability of C-N coupling and the central role of pH in stabilizing soil carbon in montane forests, offering theoretical insights for mountain forest management under elevational gradients.\u003c/p\u003e","manuscriptTitle":"Soil pH and moisture drive depth-specific patterns of SOC, TN, and TP along elevational gradients in Chinese montane forests.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 12:59:22","doi":"10.21203/rs.3.rs-7644314/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":"65df123e-fa67-4277-ab71-53d13d547d3b","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-15T15:46:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 12:59:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7644314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7644314","identity":"rs-7644314","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}