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Published on: May 24, 2024
Soil C/N ratio governs bacterial community assembly along an arid mountain elevational gradient
Yongguang Zhang1,2, Chaonan Li3, Fanjiang Zeng2
1Faculty of Agriculture, Forestry and Food Engineering, Yibin University, Yibin, China.
Soil bacterial diversity in cold-arid mountains is shaped by elevation, precipitation, and vegetation. The soil carbon-to-nitrogen ratio, influenced by vegetation, is key to bacterial community assembly in these fragile ecosystems.
Area of Science:
- Microbial Ecology
- Soil Science
- Biogeography
Background:
- Soil bacterial communities are crucial for fragile cold-arid mountain ecosystems.
- Mechanisms of bacterial community assembly along elevational gradients are poorly understood.
Purpose of the Study:
- Investigate bacterial community assembly mechanisms along an elevational gradient in the Central Kunlun Mountains.
- Identify key environmental drivers shaping soil bacterial diversity and community structure.
Main Methods:
- Analyzed soil bacterial communities across an elevational gradient (1,707–3,548 m).
- Assessed relationships between bacterial diversity (alpha and beta) and environmental factors (precipitation, vegetation, soil pH, C/N ratio).
- Utilized phylogenetic clustering to infer community assembly processes.
Main Results:
- Bacterial alpha-diversity increased with elevation; beta-diversity showed a hump-shaped pattern.
- Mean annual precipitation and vegetation cover were primary drivers, stronger than soil pH.
- Soil organic carbon-to-nitrogen ratio emerged as the dominant factor after accounting for multicollinearity.
- Higher elevations showed increased phylogenetic clustering, indicating deterministic selection linked to vegetation.
Conclusions:
- Vegetation plays a pivotal role in mediating bacterial community assembly by modulating the soil C/N ratio in arid mountains.
- Stochastic processes still dominate overall bacterial assembly, despite deterministic influences at higher elevations.
- Findings refine microbial biogeographic models, emphasizing vegetation-soil stoichiometry interplay under environmental stress for predicting climate change impacts.
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