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Updated: May 27, 2026

Measurement of the Potential Rates of Dissimilatory Nitrate Reduction to Ammonium Based on 14NH4+/15NH4+ Analyses via Sequential Conversion to N2O
Published on: October 7, 2020
Nitrogen cycle of a typical Suaeda salsa marsh ecosystem in the Yellow River estuary
Xiaojie Mou1, Zhigao Sun, Lingling Wang
1Key laboratory of Coastal Environment Processes, CAS, Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China. xjmou@163.com
Abstract:
The nitrogen (N) biological cycle of the Suaeda salsa marsh ecosystem in the Yellow River estuary was studied during 2008 to 2009. Results showed that soil N had significant seasonal fluctuations and vertical distribution. The N/P ratio (15.73 +/- 1.77) of S. salsa was less than 16, indicating that plant growth was limited by both N and P. The N absorption coefficient of S. salsa was very low (0.007), while the N utilization and cycle coefficients were high (0.824 and 0.331, respectively). The N turnover among compartments of S. salsa marsh showed that N uptake from aboveground parts and roots were 2.539 and 0.622 g/m2, respectively. The N translocation from aboveground parts to roots and from roots to soil were 2.042 and 0.076 g/m2, respectively. The N translocation from aboveground living bodies to litter was 0.497 g/m2, the annual N return from litter to soil was far less than 0.368 g/m2, and the net N mineralization in topsoil during the growing season was 0.033 g/m2. N was an important limiting factor in S. salsa marsh, and the ecosystem was classified as unstable and vulnerable. S. salsa was seemingly well adapted to the low-nutrient status and vulnerable habitat, and the nutrient enrichment due to N import from the Yellow River estuary would be a potential threat to the S. salsa marsh. Excessive nutrient loading might favor invasive species and induce severe long-term degradation of the ecosystem if human intervention measures were not taken. The N quantitative relationships determined in our study might provide a scientific basis for the establishment of effective measures.
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