Effects of vegetation type on the microbial characteristics of the fissure soil-plant systems in karst rocky desertification regions of SW China

Abstract Aims The process of karst rocky desertification has been closely related to improper land use in southwest China. Now this habitat is the subject of an important ecological restoration project. However, the changes in soil properties and microbial characteristics in response to this vegetation restoration remain poorly understood.Methods We investigated four vegetation types, including dragon fruit, Chinese pepper, walnut teak, with corn as a control, in southwest China, in 2019. We measured the impacts of these vegetation types on soil properties and microbial biomass, enzyme activity, and microbial community composition (using high-throughput sequencing technology).Results The different vegetation types had significantly different impacts on soil exchangeable Ca2+, soil organic carbon and available nutrients. The vegetation types also significantly affected microbial biomass. Soil enzyme activity, including b-1,4-glucosidase, b-1,4-N-acetylglucosaminidase, alkaline phosphatase, and catalase, were significantly different among vegetation types. All vegetation types were dominated by the bacterial phyla Acidobacteria, Proteobacteria, and Actinobacteria and the fungal phylum Ascomycota, except for corn which was dominated by the fungal phylum Mucoromycota. Non-metric multidimensional scaling (NMDS) showed that the vegetation type exhibited different microbial b-diversity, especially in winter. The vegetation type, season, and soil properties collectively explained 46% and 59% of soil bacterial and fungal community composition, respectively. The bacterial-fungal interactions under the six vegetation types were distinctly different between summer and winter.Conclusions Compared with traditional corn, the restoration of natural vegetation partially reversed KRD by improving soil properties, increasing microbial biomass, and differentiating the microbial community structures in the different vegetation types.

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Structural variability in the bulk soil, rhizosphere, and root endophyte fungal communities of Themeda japonica plants under different grades of karst rocky desertification

Plant and Soil ◽

10.1007/s11104-021-04969-y ◽

2021 ◽

Author(s):

Ming Tang ◽

Jie Liu ◽

Wenpeng Hou ◽

Reed M. Stubbendieck ◽

Han Xiong ◽

...

Keyword(s):

Fungal Communities ◽

Bulk Soil ◽

Karst Rocky Desertification ◽

Rocky Desertification ◽

Root Endophyte ◽

Structural Variability ◽

Soil Rhizosphere

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Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

Remote Sensing ◽

10.3390/rs13152935 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2935

Author(s):

Chunhua Qian ◽

Hequn Qiang ◽

Feng Wang ◽

Mingyang Li

Keyword(s):

Learning Algorithm ◽

Vegetation Type ◽

Research Area ◽

Classification Model ◽

Guizhou Province ◽

Model Accuracy ◽

Spatiotemporal Evolution ◽

Rocky Desertification ◽

The North ◽

Svm Model

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

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