PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
January 25, 2026Forests0 citationsOpen Access

Responses of Soil Enzyme Activities and Microbial Community Structure and Functions to Cyclobalanopsis gilva Afforestation in Infertile Mountainous Areas of Eastern Subtropical China

View Full Paper
SHShengyi HuangChinese Academy of ForestryYDYafei DingHebei Agricultural UniversityYXYonghong XuXuzhou Medical College

Key Points

  • The aim is to explore how afforestation with C. gilva affects soil enzyme activities and microbial community characteristics in infertile mountainous areas.
  • Selected 7-year-old C. gilva young forests and control woodlands for comparison.
  • Analyzed soil enzyme activities and microbial biomass across different soil layers.
  • Determined metagenomic profiles of rhizosphere and bulk soils.
  • Soil enzyme activities, including urease and catalase, were significantly lower in infertile areas compared to control sites.
  • Dominant microbial phyla included Proteobacteria and Acidobacteria, with specific genera like Bradyrhizobium.
  • Functional genes related to vitamin metabolism were more abundant in the rhizosphere of infertile areas.

Abstract

The effect of afforestation in infertile mountainous areas is closely related to the soil ecological environment. Soil enzyme activities and the structure and functions of microbial communities are core indicators reflecting soil quality. Clarifying the response patterns of the two to Cyclobalanopsis gilva afforestation in infertile mountainous areas can provide a key scientific basis for targeted improvement of the cultivation efficiency of C. gilva plantations under different site conditions in the eastern subtropical region of China. In this study, 7-year-old C. gilva young forests in infertile mountainous areas and control woodland areas were selected in Shouchang Forest Farm, Jiande, Zhejiang Province, located in the subtropical region of China. Soil enzyme activities and microbial biomass in different soil layers, as well as metagenomes of rhizosphere and bulk soils, were determined to explore the effects and internal correlations of site conditions on soil enzyme activities and microbial community characteristics of C. gilva forests. The results showed that the activities of urease and catalase, as well as the content of microbial biomass nitrogen in the surface soil of infertile mountainous areas, were significantly lower than those in control woodland areas. The shared dominant phyla in the two types of sites included Proteobacteria and Acidobacteria, and the shared dominant genera included Bradyrhizobium. In addition, the relative abundances of three unclassified populations of Proteobacteria and functional genes related to cofactor and vitamin metabolism in the rhizosphere soil of infertile mountainous areas were significantly higher than those in control woodland areas. Meanwhile, the dominant microbial phyla in the rhizosphere soil of infertile mountainous areas had a closer correlation with soil enzyme activities and microbial biomass. This study clarified the ecological strategy of C. gilva young forests adapting to infertile mountainous areas: by increasing the relative abundances of functional genes related to cofactor and vitamin metabolism in rhizosphere microorganisms, promoting the enrichment of microorganisms associated with soil nitrogen cycling, and enhancing the correlations between dominant microbial phyla and soil enzyme activities and microbial biomass, the nitrogen resource limitation on soil microbial activity in infertile mountainous areas is balanced. This finding provides direct guidance for optimizing the afforestation and management techniques of C. gilva in infertile mountainous areas and has important practical value for promoting forest ecological restoration.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Huang et al. (2026) studied this question.

synapsesocial.com/papers/6975b38dfeba4585c2d6efcdhttps://doi.org/10.3390/f17020154
Ask AI
Helpful
Bookmark
Share
View Full Paper