基于机器学习的Acidobacteriota和Planctomycetota的映射,使用16个S rRNA基因metabarcoding数据在俄罗斯的土壤中映射
E A Ivanova1,2, A R Suleymanov3,4,5, D A Nikitin6
1Laboratory of Soil Carbon and Microbial Ecology, Dokuchaev Soil Science Institute, Moscow, Russia. ektrnivanova@gmail.com.
Scientific reports
|July 3, 2025
概括
土壤微生物类,Acidobacteriota和Planctomycetota,在俄罗斯从北向南的丰富性呈现下降. 机器学习模型根据环境因素预测它们的分布,有助于生态系统的理解.
科学领域:
- 土壤科学 土壤科学
- 微生物学 微生物学
- 生物地理学 生物地理学
背景情况:
- 土壤微生物对生态系统健康和农业至关重要.
- 了解微生物生物地理是土壤生态系统服务的关键.
- 酸菌群和植物群是丰富的土壤类,具有重要的生态作用.
研究的目的:
- 调查俄罗斯土壤中的Acidobacteriota和Planctomycetota的空间分布.
- 使用机器学习创建预测分布图.
- 确定影响其丰富性的关键环境驱动因素.
主要方法:
- 上层土壤样本的16S rRNA基因元编码.
- 机器学习 (随机森林) 用于预测建模.
- 对气候,地形,植被,地质和土壤变量进行分析.
主要成果:
- 这两种类型的相对丰度都从北向南呈现下降.
- 酸性细菌的丰富程度与植被,温度和土壤pH值相关.
- 浮游生物丰富度主要受到气候变量的影响,随着水蒸气的增加而减少.
结论:
- 机器学习有效地可视化了土壤微生物的生物地理模式.
- 环境因素在很大程度上影响了土壤类型的分布.
- 微生物族群的大规模分布趋势甚至可以通过异质数据来确定.
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