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Scientific Reports volume 12, Article number: 14712 (2022)
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Carbon storage in wetland ecosystems is an important part of the carbon cycle of terrestrial ecosystems and provides important ecosystem services. Chaohu Wetland is a typical freshwater lake wetland in China. In this study, soil and plant samples were collected every 500 m through three sample lines of different vegetation habitats (estuarine banks, woodlands and shrub beaches) and different offshore distances, revealing the spatial distribution characteristics of soil organic carbon density (SOCD) in Chaohu wetland. The overall SOCD of Chaohu wetland was low, with different habitats ranking as Woodland > Estuary and riverside > Shrub and beach. SOCD of different offshore distances had no obvious law, and the SOCD decreased significantly with soil depth. The plant biomass was significantly higher at the woodland habitat than at other habitats. Most of soil nutrient indicators were the highest at the woodland habitat, while the estuary-riverside habitat had the highest N and P contents. Soil and plant nutrients at different offshore distances had no obvious change patterns. The contents of soil K, Ca, Mg, and N were significantly positively correlated with SOCD, but soil bulk density and pH were significantly negatively correlated with SOCD, and vegetation P content was significantly negatively correlated with SOCD. The spatial pattern of SOCD changes in this lake coastal wetland was determined by the combined effects of plant nutrients, biomass, and soil physical and chemical properties. Our results indicate Chaohu wetlands may have been experiencing serious degradation. The SOCD of Chaohu wetland is lower than that of other wetlands in China, which is mainly affected by human activities. Different offshore distances and habitat heterogeneity are the main factors affecting the soil carbon cycle of the wetland.
Wetlands account for only 5–8% of Earth’s terrestrial area, but they store about 30% of the carbon (C) pool of the global terrestrial ecosystem1. Because of the huge reserves of organic C, small changes of C pool in wetlands can greatly affect the atmospheric CO2 concentration2. Therefore, dynamic changes in wetland C storage have a significant impact on the global C cycle and climate change3,4. In addition, the increasing global warming will contribute to the sink-source transformation due to increased soil organic carbon (SOC) decomposition. Therefore, both reducing emissions and increasing the C sequestrations in the ecosystems are the important measures to alleviate excessive atmospheric CO2 concentrations5. Having strong C accumulation capability and high SOC storage5,6, wetlands have been paid more and more attention for the potential to mitigate climate change7.
Wetlands are the most ecological valuable ecosystems in the world, providing carbon sink4,5, biodiversity conservation, water purification, flood mitigation, coastal protection, and erosion control8,9,10. However, due to human disturbances, approximately half of wetlands in the world have been lost or degraded11. The major threats to wetlands are agricultural cultivation and urbanization, which have significantly reduced their SOC stocks and even resulted in loss of their ecological functioning12,13. Therefore, it is necessary to call for immediate attention to the restoration of wetland ecosystems8,12.
Soil C sequestration is one of the important functions of wetlands. Ecological restoration of wetlands has been conducted worldwide, which is considered as an effective measure to regain SOC lost as a result of human disturbances4,7,14,15. In the past few decades, great efforts were made in evaluation of soil C storages and their C sequestration potentials in the different wetland ecosystems4,7,14. The spatial distribution pattern of wetland SOC is influenced by a great number of factors, such as soil properties, climate, vegetation, hydrology, and land use patterns7,16,17. These factors and their interactions are extremely complex in wetlands ecosystems18. Therefore, there is a remarkable lack of understanding regarding the most influential factors determining SOC changes across various conditions. It is essential to conduct further studies on spatiotemporal patterns of SOC storage for different types of wetland ecosystems.
Lake Chaohu, located in the lower reach of the Yangtze River, is one of the five largest freshwater lakes in China. It is a shallow, eutrophic lake, with a surface area of19 780 km2, The ecological restoration and protection of Chaohu coastal wetland have been the important research issues for the past dozen years20,21,22. However, the field investigation data of SOC for the coastal wetland of Lake Chaohu are very limited23. This results in a remarkable lack of understanding regarding the most influential factors controlling SOC changes across various conditions. Such an understanding is especially important for managing restored wetlands for the purpose of SOC recovery. In this study, our objectives are to (1) reveal the spatial distribution pattern of SOC density, (2) determine the key influential factors controlling SOC changes, and (3) provide theoretical support for managing and utilizing wetlands with the aim of increasing soil C sequestration.
Lake Chaohu (117° 16′ 54″–117° 51′ 46″ E, 30° 25′ 28″–31° 43′ 28″ N) is located to the north of the lower Yangtze River, with a drainage area of 9258 km2 and a replenishment coefficient of 12. The replenishment water makes up 98% of the total runoff into the lake, while precipitation over the lake only accounts for 2%. The entire basin is covered by 33 rivers, 760 km2 of lake area, and 28.56 km2 of beach area. This region is located in the northern subtropical zone and is characterized by a monsoon-influenced humid subtropical climate. The average annual precipitation is 1000–1158 mm. The average annual temperature is 15.9 °C. Soils in the region are primarily derived from river and lake sediment and mountain river alluvium and consist of largely paddy soil and fluvo-aquic soil in the lowlands, calcareous soil, yellow–brown soil and purple soil in the uplands.
According to the vegetation types around Lake Chaohu, a total of 3 transects (estuary-riverside habitat, woodland habitat, and irrigation beach habitat) are set up perpendicular to the Lake Chaohu shoreline, with a distance of 2500 m between the transects, as shown in Fig. 1. According to the distribution of plants and different water level gradients, 11 plots with a total length of 5000 m are set up for each sample line at 500 m intervals. There are 3 samples of 1 m × 1 m in each plot, a total of 99 samples. Plant samples are collected from all the above-ground parts of the herbaceous plants in the sample frame, mixed and weighed to calculate the vegetation biomass, and part of the plant samples are taken to determine plant nutrients. The soil samples of 0–20 cm and 20–40 cm soil layers were collected respectively, and the samples were mixed at 3 points to determine the physical and chemical properties of the soil.
Location and distribution of sampling points in the Lake Chaohu wetland. Figure created with WeMap Version 3.9.1. http://www.rivermap.cn/index.html.
The collected soil samples were air-dried, crushed, and sifted with a 2 mm sieve. Thoroughly mixed samples were sealed in sample bags for future use. The plant samples were dried at 70 °C to constant weight, crushed, and sifted with a 100-mesh sieve. The samples were stored in sealable sample bags. To measure soil pH (H2O), distilled water and soil samples were mixed in a 2.5:1 ratio (volume : mass), shaken well, and left undisturbed for 30 min. Measurement was then performed with a pH meter. To measure soil conductivity, distilled water and soil samples were mixed in a 5:1 ratio (volume : mass), shaken well, and left undisturbed for 1 h. Measurement was then performed with an Extech II conductivity meter and a pH meter. The organic carbon and total N in plant and soil samples were quantified by combustion using an EA3000 elemental analyzer (Vector, Italy). The total P in plants and soil was determined by nitric acid-perchloric acid digestion and molybdenum-antimony anti-spectrophotometry. Available P was extracted with hydrochloric acid-ammonium fluoride extractant, whereas ammonium nitrogen and nitrate nitrogen were extracted with 2 mol·L−1 KCl solution, all of which were measured using a FIA Star 5000 flow injection analyzer (FOSS, Denmark). Soil dissolved organic carbon (DOC) and total dissolved nitrogen (DN) were extracted with 2 mol·L−1 K2SO4 solution, determined by a Multi 3100 C/N analyzer (Jena, Germany).The soil bulk density is determined by the ring knife method. K, Na, Ca, and Mg in soil and plant samples were extracted by nitric acid-perchloric acid digestion and measured by atomic absorption spectroscopy (TAS-990 AFG, Beijing Persee General Analytical Instruments, China)24.
SigmaPlot 14.0 was used to analyze the frequency distribution of SOC density in Lake Chaohu wetlands and to plot the histograms. Multi-factor analysis of variance and multiple comparisons were performed with SPSS 25.0 to find the degree of influence of habitat, offshore distance, and soil depth on SOCD. Linear regression models of SOCD vs. each variable (plant nutrients and soil physicochemical properties) were established using scatter plots and correlation analysis. Furthermore, a structural equation model was established based on soil physicochemical properties, plant nutrients, and plant biomass indicators, and the conversion factors that significantly affected SOCD were screened25.
The calculation formula of SOCD is as follows26:
In the formula, SOCDi is the soil organic carbon density of the ith layer (kg/m2); Ci is the soil organic carbon content of the ith layer (g/kg); Pi is the soil bulk density of the ith layer (g/cm3); Hi is the profile depth (cm); 10–2 is the unit conversion factor.
As shown by the multi-factor analysis of variance (Table 1), all of habitat (p = 0.005), offshore distance (p = 0.002) and soil depth (p = 0.027) had significant influence on soil organic carbon in wetlands. The estuary-riverside habitat had the lowest average SOCD (2.29 kg/m2), while the woodland habitat had the highest (2.59 kg/m2). Comparing the SOCD at different offshore distances, the average SOCD at 0 m offshore was the lowest (0.70 kg/m2), and it was the highest at 4000 m offshore (2.88 kg/m2). The average SOCD of soil depths at 0–20 cm (2.28 kg/m2) significantly higher than 20–40 cm (1.80 kg/m2) (Fig. 2).
SOCD content of different types of soil.
In different habitats, the soil nutrient content in woodland habitat was the highest, and there was no obvious rule in the change of soil nutrient content at different offshore distances (Table 2). The plant biomass and carbon content in woodland habitat were the highest, the N and P content in estuary-riverside habitat were the highest, and the change of offshore distance of plant nutrients was not obvious (Table 3).
As shown by Fig. 3, contents of K (p = 0.0057), Ca (p = 0.001), Mg (p = 0.0001), N (p < 0.0001), C/P (p < 0.0001), N/P (p = 0.0003) and TOC (p = 0.0004) in soil were significantly positively correlated with SOCD while soil bulk density (p = 0.0321) and pH (p = 0.0078) showed significant negative correlation with SOCD (p < 0.05). Among the plant nutrients, only P (p = 0.0007) showed a significant negative correlation with SOCD (Fig. 4).
Determine the soil organic carbon density (SOCD, kg·m−2) and soil nutrient factor content under each sample, and correlate between soil organic carbon density (SOCD, kg·m−2) and soil factors (mg·kg−1) (including Soil P, K, Ca, Mg, N, C/N, C/P, N/P, Bulk Density, pH, EC, TOC, TN, NH4+–N and NO3−–N) in the same space.
Determine the soil organic carbon density (SOCD, kg·m−2) and plant nutrient factor content under each sample, and correlate the soil organic carbon density (SOCD, kg·m−2) with Plant P (g·kg−1), Plant C, Plant N, Plant C/N, Plant C/P, and Plant N/P in the same space.
The best structure equation model (SEM) explained 42% of the variations in SOCD spatial heterogeneity (Fig. 5). Vegetation biomass (standardized effect size: 0.06), but not plant nutrient parameters positively contributed to SOCD spatial heterogeneity. However, soil nutrient parameters (standardized effect size: − 0.56; p < 0.01) and other parameters (pH) (standardized effect size: − 0.11) negatively contributed to SOCD spatial heterogeneity. The offshore distance was significantly negatively related to vegetation biomass (standardized effect size: − 0.86; p < 0.001), and soil other parameters (standardized effect size: − 0.52; p < 0.01).
The construction of the best structure equation model (SEM), reveals the influence of various factors on soil organic carbon density (SOCD, kg·m−2). It is concluded that offshore distance further affects the change of SOCD through the inhibition of other factors (soil factor, biomass and soil pH).
The distribution of soil organic carbon in the coastal wetlands of Lake Chaohu showed a significantly vertical decrease with soil depth, which was consistent with the previous studies27,28. The vertical distribution of SOC is dominantly affected by the primary productivity of plant community, litter yield and decomposition rate29. The source of SOC is mainly from belowground root turnover and aboveground litter input of vegetation, which mainly exist in the topsoil. This makes the SOC have characteristics of surface aggregation30,31.
Our results showed significant differences in SOCDs and SOC contents among the different habitats. This is due to the differences in plant community structures with different habits and photosynthetic fixation capacity, resulting in different quantity and quality of litter that has different effects on the carbon sink/source function of wetland soil32. The distinct differences in growth form, amount of aerenchyma, rooting depth, or timing and magnitude of primary production of different vegetation types have substantial influences on the spatial heterogeneity of SOCD in wetlands33. The spatial distribution of SOCD and accumulation of SOC in wetland ecosystems is a complicated process and is controlled by multiple factors, of which vegetation is regarded as one of the key factors33,34. In addition, the hydrologic regime has been considered as the driving force in C cycling in wetland ecosystems, which directly changes the wetland physicochemical properties, especially oxygen availability that controls decomposition of organic matter35,36. Furthermore, multiple environmental variables including soil properties, climate, and terrain have important impacts on the spatial distribution of SOC35,36. Diversity of micro-topography in natural wetland systems also plays an important role in affecting spatial distribution of SOC33,36.
Based on the optimal structural model, the offshore distance was a key factor influencing SOCD at our site. It could be mainly caused by the change of soil nutrient, pH, and vegetation biomass. Factually, to a certain extent, offshore distance reflects the changes of hydrological regime. As water-controlled ecosystems, wetland vegetations respond to the water level fluctuation. It has been well illustrated a close relationship between SOC and altitudinal gradient36,37. Zhao et al. reported that pH has a significant correlation with the SOC content35. It is in agreement to our results.
The average SOCD of the wetland in Chaohu is much lower than that of other wetlands in China (16.8 kg·m−2)38. Soil organic carbon pool is a dynamic equilibrium process and varies depending on input and output differences of carbon sources39. Due to the high sensitivity of wetland soil carbon to the changes of the surrounding environment40, Chaohu is a densely populated area with a rapidly developing located in the administrative territorial entity economy, this is an important reason for the decrease of wetland soil carbon storage. Human disturbances seriously affect the carbon sequestration capacity of Chaohu ‘s ecosystems. Therefore, balancing the economic and ecological relationship is important for stabilizing the carbon cycle in the region around Lake Chaohu.
The SOCD in coastal wetland of Lake Chaohu was significantly higher in topsoil than that in subsoil, which is mainly related to the distribution of litter and root system. SOCD was higher in woodland habitat than in the others. SOCD within 500-m offshore distance was lower than 500 m away. With the increase of offshore distance, SOCD increased nonlinearly that was related to soil pH, vegetation biomass and soil nutrients. There existed great spatial heterogeneity of soil organic carbon distributions in wetland of Lake Chaohu. However, its internal driving mechanism is not entirely clear. Therefore further researches are needed on the factors affecting SOC distribution in this coastal wetlands.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Mitsch, W. J. et al. Wetlands, carbon, and climate change. Landsc. Ecol. 28, 583–597. https://doi.org/10.1007/s10980-012-9758-8 (2013).
Article Google Scholar
Koehler, A. K., Sottocornola, M. & Kiely, G. How strong is the current carbon sequestration of an Atlantic blanket bog?. Glob. Change Biol. 17, 309–319. https://doi.org/10.1111/j.1365-2486.2010.02180.x (2015).
Article ADS Google Scholar
Chmura, G. L., Anisfeld, S. C., Cahoon, D. R. & Lynch, J. C. Global carbon sequestration in tidal, saline wetland soils. Global Biogeochem. Cycles 17, 1–12. https://doi.org/10.1029/2002GB001917 (2003).
Article CAS Google Scholar
Dong, H. Y., Qian, L. W., Yan, J. F. & Wang, L. Evaluation of the carbon accumulation capability and carbon storage of different types of wetlands in the Nanhui tidal flat of the Yangtze River estuary. Environ. Monit. Assess. 192, 585. https://doi.org/10.1007/s10661-020-08547-0 (2020).
Article CAS PubMed Google Scholar
Zaher, H., Sabir, M., Benjelloun, H. & Paul-Igor, H. Effect of forest land use change on carbohydrates, physical soil quality and carbon stocks in Moroccan cedar area. J. Environ. Manage. 254, 109544. https://doi.org/10.1016/j.jenvman.2019.109544 (2020).
Article CAS PubMed Google Scholar
Friborg, T. Siberian wetlands: Where a sink is a source. Geophys. Res. Lett. 30, 2129. https://doi.org/10.1029/2003GL017797 (2003).
Article ADS CAS Google Scholar
Dayathilake, D., Lokupitiya, E. & Wijeratne, V. Estimation of soil carbon stocks of urban freshwater wetlands in the Colombo Ramsar Wetland City and their potential role in climate change mitigation. Wetlands. https://doi.org/10.1007/s13157-021-01424-7 (2021).
Article Google Scholar
Li, X. W. et al. How important are the wetlands in the middle-lower Yangtze River region: An ecosystem service valuation approach. Ecosyst. Serv. 10, 54–60. https://doi.org/10.1016/j.ecoser.2014.09.004 (2014).
Article Google Scholar
Liu, K. et al. Diversity of vascular plant and classification system of vegetation in wetlands of Anhui Province. Acta Ecol. Sin. 34, 5434–5444. https://doi.org/10.5846/stxb201301160109 (2014).
Article Google Scholar
Liu, H., Zheng, L., Wu, J. & Liao, Y. H. Past and future ecosystem service trade-offs in Poyang Lake Basin under different land use policy scenarios. Arab. J. Geosci. 13, 46. https://doi.org/10.1007/s12517-019-5004-x (2020).
Article Google Scholar
Dixon, M. J. R. et al. Tracking global change in ecosystem area: the Wetland Extent Trends index. Biol. Conserv. 193, 27–35. https://doi.org/10.1016/j.biocon.2015.10.023 (2016).
Article Google Scholar
Yang, X., Liu, S., Jia, C., Liu, Y. & Yu, C. C. Vulnerability assessment and management planning for the ecological environment in urban wetlands. J. Environ. Manag. 298, 113540. https://doi.org/10.1016/j.jenvman.2021.113540 (2021).
Article Google Scholar
Ghosh, S. & Das, A. Urban expansion induced vulnerability assessment of East Kolkata Wetland using Fuzzy MCDM method. Remote Sens. Appl. Soc. Environ. 13, 191–203. https://doi.org/10.1016/j.rsase.2018.10.014 (2019).
Article Google Scholar
Means, M. M., Ahn, C., Korol, A. R. & Williams, L. D. Carbon storage potential by four macrophytes as affected by planting diversity in a created wetland. J. Environ. Manag. 165, 133–139. https://doi.org/10.1016/j.jenvman.2015.09.016 (2016).
Article Google Scholar
Fenstermacher, D. E., Rabenhorst, M. C., Lang, M. W., McCarty, G. W. & Needelman, B. A. Carbon in natural, cultivated, and restored depressional wetlands in the Mid-Atlantic Coastal Plain. J. Environ. Qual. 45, 743–750. https://doi.org/10.2134/jeq2015.04.0186 (2016).
Article CAS PubMed Google Scholar
Abegaz, A., Winowiecki, L. A., Vågen, T., Langan, S. & Smith, J. U. Spatial and temporal dynamics of soil organic carbon in landscapes of the upper Blue Nile Basin of the Ethiopian Highlands. Agric. Ecosyst. Environ. 34, 190–208. https://doi.org/10.1016/j.agee.2015.11.019 (2016).
Article CAS Google Scholar
Xie, E., Zhang, Y., Huang, B., Zhao, Y. & Qu, M. Spatiotemporal variations in soil organic carbon and their drivers in southeastern China during 1981–2011. Soil Tillage Res. 205, 104763. https://doi.org/10.1016/j.still.2020.104763 (2021).
Article Google Scholar
Jackson, R. B. et al. The ecology of soil carbon: Pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445. https://doi.org/10.1146/annurev-ecolsys-112414-054234 (2017).
Article Google Scholar
Sun, K. K., Chen, X., Dong, X. H. & Yang, X. D. Spatiotemporal patterns of carbon sequestration in a large shallow lake, Lake Chaohu: Evidence from multiple-core records. Limnologica 81, 125748. https://doi.org/10.1016/j.limno.2020.125748 (2020).
Article CAS Google Scholar
Chen, X., Yang, X. D., Dong, X. H. & Liu, E. F. Environmental changes in Lake Chaohu (southeast, China) since the mid 20th century: The interactive impacts of nutrients, hydrology and climate. Limnologica. 43, 10–17. https://doi.org/10.1016/j.limno.2012.03.002 (2013).
Article CAS Google Scholar
Yu, J. H. et al. Temporal changes in fractions and loading of sediment nitrogen during the holistic growth period of Phragmites australis in littoral Lake Chaohu, China. J. Lake Sci. 33, 1467–1477. https://doi.org/10.18307/2021.0514 (2021).
Article CAS Google Scholar
Zhang, M. & Kong, F. X. The process, spatial and temporal distrbition and mitigation strategies of the eutrophication of Lake Chaohu (1984–2013). J. Lake Sci. 27, 791–798. https://doi.org/10.18307/2015.0505 (2015).
Article Google Scholar
Teng, Z., Cao, X. Q., Sun, M. Y., Li, P. X. & Xu, X. N. Effect of different ecological restoration patterns on soil labile organic carbon and carbon pool management index of lakeside wetland of Lake Chaohu. Ecol. Environ. Sci. 28, 752–760. https://doi.org/10.16258/j.cnki.1674-5906.2019.04.014 (2019).
Article Google Scholar
Wang, J. J. et al. Effects of simulated nitrogen deposition on soil microbial biomass and community function in subtropical evergreen broad-leaved forest. For. Syst. 28, e018. https://doi.org/10.5424/fs/2019283-15404 (2019).
Article Google Scholar
Yang, Y. et al. Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Glob. Change Biol. 14, 1592–1599. https://doi.org/10.1111/J.1365-2486.2008.01591.X (2008).
Article ADS Google Scholar
Li, J. et al. The spatial distribution of soil organic carbon density and carbon storage in Baiyangdian wetland. Acta Ecologica Sinica 40, 8928–8935. https://doi.org/10.16258/j.cnki.1674-5906.2019.04.014 (2020).
Article Google Scholar
Ma, W. W. et al. Variations of organic carbon storage in vegetation-soil systems during vegetation degradation in the Gahai wetland, China. Chin. J. Appl. Ecol. 29, 3900–3906. https://doi.org/10.13287/j.1001-9332.201812.003 (2018).
Article Google Scholar
Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297. https://doi.org/10.1038/ngeo1123 (2015).
Article ADS CAS Google Scholar
Cao, L. et al. Deposition and burial of organic carbon in coastal salt marsh: Research progress. Chin. J. Appl. Ecol. 24, 2040–2048. https://doi.org/10.1038/ngeo1123 (2013).
Article CAS Google Scholar
Liao, X. J. et al. Distribution pattern of soil organic carbon contents in the coastal wetlands in Eastern Fujian. Wetl. Sci. 11, 192–197. https://doi.org/10.3969/j.issn.1672-5948.2013.02.007 (2013).
Article Google Scholar
Kong, F. L., Min, X. I., Yue, L. I., Li-Hua, X. U. & Feng, X. M. Distribution and storage of DOC in a typical annular wetland of Sanjiang Plain. Bull. Soil Water Conserv. 33, 176–179. https://doi.org/10.3969/j.issn.1672-5948.2013.02.007 (2013).
Article Google Scholar
He, L. P., Meng, G. T., Li, G. X., Li, P. R. & Chai, Y. Soil organic carbon and its distribution characteristics in the soil profile under different vegetation recovery modes in toutang small watershed of Jinsha river. Resour. Environ. Yangtze Basin 25, 476–485. https://doi.org/10.13248/j.cnki.wetlandsci.2013.02.003 (2016).
Article Google Scholar
Bernal, B. & Mitsch, W. J. A comparison of soil carbon pools and profiles in wetlands in Costa Rica and Ohio. Ecol. Eng. 34, 311–323. https://doi.org/10.1016/j.ecoleng.2008.09.005 (2008).
Article Google Scholar
Dong, J. et al. A novel organic carbon accumulation mechanism in croplands in the Yellow River Delta, China. Sci. Total Environ. 806, 150629. https://doi.org/10.1016/j.scitotenv.2021.150629 (2021).
Article CAS PubMed Google Scholar
Wang, S., Adhikari, K., Wang, Q., Jin, X. & Li, H. Role of environmental variables in the spatial distribution of soil carbon (C), nitrogen (N), and C:N ratio from the northeastern coastal agroecosystems in China. Ecol. Indic. 84, 263–272. https://doi.org/10.1016/j.ecolind.2017.08.046 (2018).
Article CAS Google Scholar
Zhao, Q. et al. Soil organic carbon content and stock in wetlands with different hydrologic conditions in the Yellow River Delta, China. Ecohydrol. Hydrobiol. 20, 537–547. https://doi.org/10.1016/j.ecohyd.2019.10.008 (2020).
Article Google Scholar
Weishampel, P., Kolka, R. & King, J. Y. Carbon pools and productivity in a 1-km2 heterogeneous forest and peatland mosaic in Minnesota, USA. For. Ecol. Manag. 257, 747–754. https://doi.org/10.1016/j.foreco.2008.10.008 (2009).
Article Google Scholar
Yu, D. S., Shi, X. Z., Wang, H. J., Sun, W. X. & Zhao, Y. C. Regional patterns of soil organic carbon stocks in China. J. Environ. Manag. 85, 680–689. https://doi.org/10.1016/j.jenvman.2006.09.020 (2007).
Article CAS Google Scholar
Wu, Y. et al. Elevation gradient characteristics and impact factors of soil carbon fractions in the Pinus taiwanensis Hayata forests of Daiyun Mountain. Acta Ecol. Sinica. 40, 5761–5770. https://doi.org/10.5846/stxb201908161713 (2020).
Article Google Scholar
Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627. https://doi.org/10.1126/science.1097396 (2004).
Article ADS CAS PubMed Google Scholar
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This research was funded by the Natural Science Foundation of Anhui Province (No. 1908085ME140), the Anhui Province Peak Discipline Scientific Research Project (No. 2021136), and by the National Science Foundation of China (No. 31370626).
School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, 230036, China
Xiaojie Yao & Xiaoniu Xu
College of Architecture and Urban Planning, Anhui Jianzhu University, Hefei, 230601, China
Xiaojie Yao, Xinyun Xie & Dan Jiang
Anhui Academy of Forestry, Hefei, 230031, China
Jingjing Wang
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Conceptualization: X.Xu and X.Y. Performed the experiments: J.W. and X.Xie Analyzed the data: X.Y. Writing—original draft preparation: X.Y. and D.J. Writing—review and editing: X.Xu. All authors have read and agreed to the published version of the manuscript.
Correspondence to Xiaoniu Xu.
The authors declare no competing interests.
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Yao, X., Wang, J., Xie, X. et al. Distribution of SOCD along different offshore distances in China’s fresh-water lake-Chaohu under different habitats. Sci Rep 12, 14712 (2022). https://doi.org/10.1038/s41598-022-18260-2
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