Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The R2 and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology

Plant species and associated root nutritional traits influence soil dominant bacteria in coastal wetlands across China

11 página.- 6 figuras.- referenciasClimate and edaphic properties drive the biogeographic distribution of dominant soil microbial phylotypes in terrestrial ecosystems. However, the impact of plant species and their root nutritional traits on microbial distribution in coastal wetlands remains unclear.Here, we investigated the nutritional traits of 100 halophyte root samples and the bacterial communities in the corresponding soil samples from coastal wetlands across eastern China. This study spans 22° of latitude, covering over 2500 km from north to south.We found that 1% of soil bacterial phylotypes accounted for nearly 30% of the soil bacterial community abundance, suggesting that a few bacterial phylotypes dominated the coastal wetlands. These dominated phylotypes could be grouped into three ecological clusters as per their preference over climatic (temperature and precipitation), edaphic (soil carbon and nitrogen), and plant factors (halophyte vegetation, root carbon, and nitrogen). We further provide novel evidence that plant root nutritional traits, especially root C and N, can strongly influence the distribution of these ecological clusters.Taken together, our study provides solid evidence of revealing the dominance of specific bacterial phylotypes and the complex interactions with their environment, highlighting the importance of plant root nutritional traits on biogeographic distribution of soil microbiome in coastal wetland ecosystems.This work was funded by the National Key R&D Program of China (2022YFF1301000) and Fundamental Research Funds of CAF (CAFYBB2020QB008). Plant–microbial interactions study in B.K.S laboratory is supported by Australian Research Council (DP230101448)Peer reviewe