Spatial distribution and stoichiometry of soil carbon, nitrogen and phosphorus along an elevation gradient in a wetland in China

Abstract

Despite extensive studies on how environmental factors influence plant nutrient distribution and stoichiometry, it remains unclear how elevation affects soil distribution and stoichiometry. Here, patterns of the spatial distribution and stoichiometry of soil carbon (C), nitrogen (N) and phosphorus (P) were studied (with 123 soil samples) along a small‐scale elevation gradient in the wetland of East Dongting Lake, China. Plant nutrient concentrations (C, N and P), soil stoichiometry (C:N, C:P and N:P ratios) and their relations with other soil properties were analysed. In addition, the spatial distributions of plant nutrient concentrations and stoichiometry were evaluated geostatistically with regression kriging, entailing estimation of regression parameters by generalized least squares and ordinary kriging of the residuals. Soil C, N and P concentrations and stoichiometry had moderate to strong spatial dependence, and the ratios of C:P and N:P had similar patterns of distribution to soil organic carbon (SOC) and total nitrogen (TN). The SOC, TN, C:N, C:P and N:P ratios increased with increasing elevation, whereas total phosphorus (TP) showed no marked change. Furthermore, SOC, TN, C:N, C:P and N:P decreased considerably with increasing duration of submergence, soil moisture and pH. Therefore, elevation appeared to influence the distribution of plant nutrients and stoichiometry as a result of changing soil moisture and duration of submergence. Highlights Study showed how elevation influences plant nutrient distribution and stoichiometry. Regression kriging was used to map soil properties. Plant nutrients and stoichiometry decreased with increasing duration of submergence. Elevation affected nutrient distribution because of soil moisture and water regime.

Differences in sediment nitrogen and phosphorus distribution between riverside and lakeside wetlands in a river-connected lake

“…In this study, the average TN concentration in the surface sediment (0-20 cm) was 1.05 ± 0.66 g kg −1 , and the average TP concentration in the surface sediment was 0.49 ± 0.15 g kg −1 , which were lower than the concentrations (TN, 1.73 ± 0.79 g kg −1 ; TP, 0.89 ± 0.12 g kg −1 ) in previous studies conducted in the littoral zone of East Dongting Lake [25]. This may be because the time of sediment sampling was spring in our study and winter in a previous study.…”

“…Lakeside wetlands are subject to longer inundation time and stronger nitrogen sink than riverside wetlands, resulting in higher sediment nitrogen concentration. This phenomenon may also be attributed to the dominant plants in lakeside wetlands being more luxuriant than those in riverside wetlands, with larger biomass and more litter released nitrogen into sediments [25,40]. Meanwhile, in the surface sediments, the correlations between TN and TP on the riverside were positive, and there was no correlation between TN and TP on the lakeside of Dongting Lake.…”

Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

“…Recently, numerous studies have discussed the eco-stoichiometry of soil C, N, and P in terrestrial ecosystems (e.g., wetland, grassland, forest, and desert oasis). The results indicated that soil C:N can predict the nitrate leaching degree of forest soil, while the soil N:P ratio is able to reflect the degree of vegetation disturbance [6][7][8]. C and N levels play a decisive role in the changing process of regional soil C, N, and P eco-stoichiometric characteristics [9,10].…”

Soil Penetration Resistance after One-Time Inversion Tillage: A Spatio-Temporal Analysis at the Field Scale

“…Compositional kriging acknowledges the constant sum and non-negativity constraints of compositional data such as fractions of soil texture [66,67]. For carbon content, the ordinary kriging method was used [68,69]. For this purpose, automatically generated variograms were adapted to the point pairs and the values were regionalized by means of the autocorrelative behavior described in this way.…”