微囊的丰富性可以通过环境细菌群落来预测:以数据为导向的方法.
Mingyeong Kang1, Dong-Kyun Kim2, Ve Van Le1
1Cell Factory Research Center, Korea Research Institute of Bioscience and Biotechnology, 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea; Department of Environmental Biotechnology, KRIBB School of Biotechnology, Korea University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea.
Journal of environmental management
|August 10, 2024
概括
结合细菌社区数据显著改善了蓝藻有害藻类繁殖 (cyanoHAB) 预测模型. 这项研究强调了微生物与环境因素对准确的cyanoHAB预测的关键作用.
科学领域:
- 环境微生物学环境微生物学
- 水生生态学 水生生态学
- 机器学习在环境科学中的应用.
背景情况:
- 菌有害藻类繁殖 (cyanoHABs) 的流行率不断增加,需要改进预测模型.
- 现有的cyanoHAB模型主要依赖于环境数据,往往忽视了细菌群落的重大影响.
- 了解微生物相互作用是提高蓝色HAB预测准确性的关键.
研究的目的:
- 开发和评估一种机器学习模型,以利用细菌社区和环境数据来预测Microcystis动态.
- 评估细菌群落与环境因素在cyanoHAB预测中的相对重要性.
- 确定影响微囊花的关键微生物参与者和环境驱动因素.
主要方法:
- 使用多层感知器 (MLP) 机器学习模型进行预测.
- 综合了每周的水质数据与来自大水库和中东河的细菌社区数据.
- 进行了后期分析,以确定不同因素的贡献,并确定了关键的微生物种群.
主要成果:
- 结合细菌和环境数据的MLP模型实现了较高的R2 (0.97) 与单独的环境数据 (0.78) 相比.
- 源被确定为微囊花的比更关键.
- 细菌家族Microscillaceae在MLP和MLR模型中显示出与Microcystis动态的最强相关性.
结论:
- 细菌社区数据显著提高了cyanoHAB模型的预测能力.
- 微生物社区的组成是理解和预测cyanoHABs的一个关键因素.
- 这种方法通过结合环境细菌数据,为更准确,更可靠的cyanoHAB预测提供了一条途径.
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