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时间记忆机制和生态系统碳流的生物特异性驱动因素:从可解释的深度学习建模的见解
Teng Ma1,2, Wei He1, Shuangxi Fang1,2
1Zhejiang Carbon Neutral Innovation Institute & Zhejiang International Cooperation Base for Science and Technology on Carbon Emission Reduction and Monitoring, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
Global change biology
|January 27, 2026
概括
生态系统的碳动力学是复杂的,但新的LSTM-Attention模型揭示了跨生物体的独特时间记忆模式. 了解这些模式是预测全球变化下的碳气候反的关键.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 生态系统的碳动力学涉及复杂的时间依赖性和环境相互作用,在整个生物群体中了解得很少.
- 准确量化这些过程对于气候建模至关重要.
研究的目的:
- 开发和应用一个可解释的AI框架,以揭示生态系统碳流量的时间依赖.
- 为了识别不同的时间记忆模式和生物体特定的碳交换环境驱动因素.
主要方法:
- 开发了一个LSTM-Attention框架,集成LSTM,注意力机制和基于梯度的归因.
- 分析了来自北美八个生物群的71个地点的状共变量流量数据.
- 采用了废除实验来验证时间记忆的作用.
主要成果:
- 确定了三个时间记忆模式:短期 (草原),长期 (森林,湿地) 和U形双重记忆 (农田,常青森林).
- 发现了生物群特异性驱动因素 (例如,湿地中的土壤水分,常青森林中的辐射) 和关键的非线性相互作用.
- 证实时间内存是精确碳流模型和驾驶员识别的组成部分.
结论:
- 时间记忆在调节生态系统对环境驱动因素的碳流反应方面发挥着基本作用.
- 对生物群特定时间控制的机械洞察力提高了对陆地碳气候反的理解.
- 该框架增强了在全球变化场景下生态系统运作的预测.
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