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100年的人类影响导致淡水功能生物多样性的变化

Niamh Eastwood¹, Jiarui Zhou¹, Romain Derelle¹

¹Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, United Kingdom.

|November 7, 2023

概括

生物多样性丧失正在加速,威胁着生态系统服务. 我们的AI框架揭示了农药和极端天气驱动淡水生态系统的变化,突出了系统性的长期生物多样性监测的必要性.

科学领域:

环境科学环境科学
生态生态学生态生态学
计算生物学计算生物学

背景情况:

全球生物多样性正在迅速下降,危及生态系统的基本服务.
现有的监测方法往往无法捕捉到复杂的生态系统变化.
了解生物多样性变化的驱动因素对于有效的保护至关重要.

研究的目的:

开发和应用人工智能驱动的框架来分析长期生物多样性动态.
研究气候变化和化学污染对淡水生态系统的影响.
将社区级生物多样性评估的有效性与传统指数进行比较.

主要方法:

利用概念AI框架将生物多样性动态与随时间变化的非生物变化联系起来.
应用可解释的网络模型与多式联络学习到100年的淡水生态系统数据.
分析了社区一级的功能生物多样性,使用多地点元编码,与气候和化学污染记录相关联.

主要成果:

淡水社区的结构和功能在100年内发生了重大变化,没有恢复到原来的状态.
杀虫剂,杀菌剂,极端温度和降水解释了高达90%的功能生物多样性变化.
社区层面的生物多样性评估准确地识别了生态系统的变化,与热带二原子指数等传统指数不同.

结论:

分析长期趋势的高通量系统方法优于以物种为中心的调查来确定生物多样性丧失的驱动因素.
功能生物多样性的人工智能驱动分析提供了一个强大的方法来检测和理解生态系统的破坏.
这种方法对于开发变革性解决方案至关重要,以保护生物多样性和生态系统功能为后代提供.

关键词:

计算生物学是计算生物学.生态学生态学是什么淡水淡水水的情况.功能生物多样性的生物多样性.机器学习是机器学习.多位元元位元编码的多位元元编码没有,没有,没有.这是DNA的DNA的DNA.系统生物学系统生物学

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