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将微生物社区数据集成到生态系统规模模型中,以预测面对气候变化的垃圾分解

Katherine S Rocci^1,2, Derek Pierson³, Fiona V Jevon⁴

¹Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA.

Global change biology

|July 17, 2025

概括

这项研究将微生物数据集成到生态系统模型中,用于预测叶子垃圾分解. 纳入这些驱动因素可以提高模型的准确性,并揭示气候变化对碳循环的影响.

科学领域:

生态生态学生态生态学
生物地质化学生物地质化学
计算生物学计算生物学

背景情况:

垃圾分解是影响全球碳流量的关键生态系统过程.
生态系统模型预测了分解,但往往缺乏微生物社区数据.
整合微生物数据可以提高气候变化预测模型的准确性.

研究的目的:

用实证微生物社区数据校准和验证微生物-矿物碳稳定 (MIMICS) 模型.
评估结合微生物驱动因素对预测叶子垃圾分解的影响.
在气候变化场景下评估模型性能.

主要方法:

在10个美国国家生态观测网 (NEON) 站点进行了叶子垃圾袋实验.
使用实证分解速率和微生物群体数据 (复制体与寡质体比率) 校准了MIMICS模型.
验证了校准模型并将其与SSP 3-7.0气候变化场景进行了测试.

主要成果:

纳入经验性微生物驱动因素改善或匹配了叶子垃圾分解的模型预测.
与传统方法相比,模型校准揭示了与传统方法相比不同的潜在生态动态.
气候变化模拟显示,在某些地点,垃圾质量损失的潜在增加高达5%.

结论:

将实证微生物数据集成到生态系统模型中,可以提高对垃圾分解的预测.
这种方法提高了对气候变化下的碳循环气候反的理解.
该研究为将微生物功能组数据纳入基于过程的模型提供了一个框架.

关键词:

气候变化带来的影响数据与模型的整合生态系统规模模型垃圾的分解垃圾的分解垃圾袋垃圾袋垃圾袋微生物的特征微生物的特征模型校准模型的校准.土壤微生物群落土壤微生物群落

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