通过神经网络和决策树学习预测微生物生物多样性的多个值和轨迹
Laurent Fontaine1,2, Maryia Khomich1,3, Tom Andersen1,2
1Section for Aquatic Biology and Toxicology, Department of Biosciences, University of Oslo, Oslo, Norway.
ISME communications
|November 8, 2023
概括
生态学研究往往忽略了复杂的关系. 机器学习揭示了淡水棕色化和微生物多样性之间的非单调联系,突出了北极湖泊的关键变化.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 微生物学 微生物学
背景情况:
- 生态学研究经常假设生物多样性和环境因素之间存在线性关系.
- 然而,非线性 (非单调性) 关系越来越多地被认为是自然生态系统中普遍存在的.
研究的目的:
- 为了研究微生物多样性与斯堪的纳维亚北极湖泊的淡水棕色化之间的非单调关联.
- 将机器学习的解释能力与检测这些非单调模式的传统方法进行比较.
主要方法:
- 应用机器学习算法来分析70个北极湖泊的数据.
- 评估细菌丰富性和均性 (α-多样性) 和社区组成 (β-多样性) 与环境梯度相关,特别是淡水色.
主要成果:
- 细菌的α-多样性表现出非单调的趋势,在淡水色的中间水平上达到顶峰.
- 机器学习模型解释了多达45%的细菌群体组成差异,远高于假定单调关系的传统方法 (5%).
- 个别细菌种群的非单一反应为观察到的社区级模式做出了贡献.
结论:
- 非单调的关系对于理解微生物多样性对环境变化的反应至关重要,例如淡水色.
- 机器学习对于准确检测这些复杂的生态模式至关重要.
- 预测的细菌多样性的变化对北极淡水生态系统的新陈代谢和温室气体的产生有影响.
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