全球光合作用能力由酶动力学和生态-生态-环境驱动因素共同决定
Zhengbing Yan1,2,3, Matteo Detto4, Zhengfei Guo2
1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.
Fundamental research
|January 30, 2026
概括
全球光合作用能力 (RuBisCO最大炭化氧化率,Vc,max) 可以通过将酶动力学和活性RuBisCO量结合在一起的模型更好地预测. 这种方法改善了气候变化下的陆地碳循环模拟.
科学领域:
- 生态生态学 生态生态学
- 植物生理学 植物生理学
- 生物地质化学生物地质化学
背景情况:
- 在气候变化下的陆地生态系统光合作用和碳循环的准确模拟需要了解全球光合作用能力 (Vc,max) 的变化.
- 目前的模型缺乏对驱动Vc,max的因素的整体评估,阻碍了准确的气候变化影响预测.
研究的目的:
- 测试假设Vc,max是由温度相关的酶动力学和活性RuBisCO (Vc,max25) 的数量决定的.
- 开发和评估一个用于预测全球Vc,max变量的统计模型.
- 确定理论模型中缺失的机制,以改进Vc,max预测.
主要方法:
- 编制了来自428个地点的7339个叶子气交换测量的全球数据集.
- 开发了一种半经验统计模型,其中包含了酶动力学和Vc,max25.25.
- 将统计模型的性能与理论最佳性模型进行比较.
主要成果:
- 统计模型解释了全球Vc,max变量的78%,明显优于理论模型 (67%).
- 仅仅酶动力学就解释了55%的Vc,max变化.
- 理论模型对Vc,max25可变性 (3%) 的表征很差,突出了地下资源限制的重要性.
结论:
- 酶动力学和活性RuBisCO量的综合方法显著改善了全球Vc,max.的预测.
- 对光合作用机械的地下资源限制对于理解Vc,max生物地理和改进理论模型至关重要.
- 这些发现为基于陆地光合作用和碳循环的过程模型的基准测试提供了基础.
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