Spatial Distribution of Soil Organic Carbon and Total Nitrogen in a Ramsar Wetland, Dafeng Milu National Nature Reserve

Yao, Xiuying; Yan, Dandan; Li, Jingtai; Liu, Yao; Sheng, Yufeng; Xie, Siying; Luan, Zhaoqing

doi:10.3390/w14020197

Cited by 5 publications

(5 citation statements)

References 40 publications

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“…However, approximately one-quarter of the spatial variability remains unexplained due to a lack of soil temperature, soil respiration, soil mineral composition, and soil microbial data. Relevant studies have found that under similar external conditions, soil particle size can affect soil nutrients [50]. Soil temperature is an important factor that affects microbial respiration, as well as the absorption and decomposition rate of soil TN.…”

Section: Other Factors Of Soil Tnmentioning

confidence: 99%

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

et al. 2022

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Soil nitrogen is very important for crop growth and development. However, the factors affecting the three-dimensional spatial distribution of soil total nitrogen (TN), particularly in coal mining subsidence areas, are unclear. In this study, Markov geostatistics was used to analyse the three-dimensional spatial distribution characteristics and influential factors of TN by examining 180 soil samples from the Zhaogu mine in China. The results showed that the TN content was significantly different at different soil depths (0–20, 20–40, 40–60 cm) and decreased with increasing soil depth. The variation coefficient of the TN content decreased gradually from top to bottom, ranging from 18.18 to 25.62%. In addition, the TN content was greatly affected by mining subsidence, rainfall, irrigation, fertilization and management mode. The factors that influenced the TN content also varied across different slope positions. The TN content of the upslope was the highest, and the TN content of the middle slope was the lowest. These results can provide research ideas and technical countermeasures for ecological environment improvement and sustainable land development in coal mining subsidence areas.

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Section: Other Factors Of Soil Tnmentioning

confidence: 99%

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

et al. 2022

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“…In addition, with increasing depth, the amount of decomposition products of S. alterniflora roots and other withered organs gradually decreased, the organic matter content of the sediment decreased, the sediment structure became denser, and the BD increased. Furthermore, BD is associated with soil texture, and a decrease in clay content and an increase in sand content resulted in a higher BD [38,39].…”

Section: Driving Factors Of Changes In Physicochemical Indexes Of Sed...mentioning

confidence: 99%

“…The change in sediment MC under S. alterniflora vegetation was mainly affected by tidal action. Accumulated water on the sediment surface increases with depth, enhancing soil compaction while diminishing infiltration, resulting in reduced MC [39,40].…”

Section: Driving Factors Of Changes In Physicochemical Indexes Of Sed...mentioning

confidence: 99%

“…A higher sediment water content has been reported to affect plant growth and microbial decomposition, which are conducive to OC accumulation. Long-term flooding of S. alterniflora wetlands may increase the root density of S. alterniflora, which helps to aggregate more organic matter [39]. The moisture status of the sediment can markedly affect the mineralization and decomposition of OC by affecting its permeability, thereby affecting its OC content.…”

Section: Response Mechanism Of Organic Carbon Content To Influencing ...mentioning

confidence: 99%

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Distribution Characteristics and Main Influencing Factors of Organic Carbon in Sediments of Spartina Alterniflora Wetlands along the Northern Jiangsu Coast, China

Zhang,

Lv,

Shu

et al. 2024

Land

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In this study, columnar sediment samples were collected from north to south along the northern Jiangsu coast, China, under Spartina alterniflora vegetation in four sample areas: Chuandong Port (Area-1), Tiaozini Scenic Area (Area-2), Yangkou Town (Area-3), and Meiledi Marine Park (Area-4). Organic carbon (OC), nutrient elements including total nitrogen (TN), total phosphorus (TP), and total sulfur (TS), and physicochemical properties including pH, salinity (Sal), moisture content (MC), and bulk density (BD) were measured. Pearson’s correlation analysis was performed to explore the correlation between OC content and sedimentary physicochemical indexes, and the partial least squares regression (PLSR) model was used to analyze the factors affecting changes in OC content. The results found that the OC content of columnar sediments of S. alterniflora decreased with increasing depth in all four areas. The OC content in the four sample areas was mainly affected by the TN, pH, MC, TP, and burial depth. In particular, TN, MC, TP, TS, and clay content positively affected OC, whereas burial depth, pH, silt content, BD, sand content, and Sal negatively affected OC. The results of this study provide a valuable reference for evaluating the role of coastal wetlands in the global carbon cycle.

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“…Second, the expansion of S. alterniflora is a complex process of change, which is affected by many factors. The transformation of the transient spatial pattern of S. alterniflora from vegetation colonization to expansion only took 2-3 years, and soil nutrients may be the main controlling factor (Xie et al, 2019;Yao et al, 2022b). When making the S. alterniflora suitability atlas, the present study comprehensively considered the effects of tidal inundation, soil salinity, artificial ditches, and elkaffected areas on the distribution of S. alterniflora based on previous research results.…”

Section: Uncertainty and Limitations Of S Alterniflora Predictionsmentioning

confidence: 99%

Examining the expansion of Spartina alterniflora in coastal wetlands using an MCE-CA-Markov model

Yan

Xie

et al. 2022

Front. Mar. Sci.

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The spread of Spartina alterniflora (smooth cordgrass) has put biodiversity and ecosystem function at risk since it was introduced to China just a few decades ago. A better understanding of how the range of S. alterniflora will expand in the future will help manage the invasion of this species in coastal wetlands. However, it is difficult to model the future extent of Spartina saltmarshes in China. To address this issue, we combined multi criteria evaluation with traditional CA Markov model to provide robust forecasting of the spatial expansion of S. alterniflora for the next ten years, at Dafeng Milu National Nature Reserve, Jiangsu, China. Our results showed that, compared with the interpretation results of high-resolution remote sensing images in 2020, the kappa coefficient of verification accuracy was 82.63%, indicating that the MCE-CA-Markov model has good prediction results. Therefore, the model can forecast the expansion process of S. alterniflora over the next ten years. The model predicts that the area of S. alterniflora continued to decrease from 910.25 ha in 2020 to 881.21 ha in 2030. The spatial distribution of S. alterniflora has been decreasing on the landward side while it has been expanding towards the sea on the seaward side. This study provides some suggestions for effective management and control of invasive species, which could be important for wetland biodiversity conservation and resource management.

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Spatial Distribution of Soil Organic Carbon and Total Nitrogen in a Ramsar Wetland, Dafeng Milu National Nature Reserve

Cited by 5 publications

References 40 publications

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area

Distribution Characteristics and Main Influencing Factors of Organic Carbon in Sediments of Spartina Alterniflora Wetlands along the Northern Jiangsu Coast, China

Examining the expansion of Spartina alterniflora in coastal wetlands using an MCE-CA-Markov model

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