Vegetation carbon use efficiency (CUE), the ratio between net primary productivity (NPP) and gross primary productivity (GPP), provides insight into the ability of ecosystems to transfer large amounts of carbon (C) from the atmosphere to potential C-sinks. Although the patterns and feedback of CUE on climate change have been previously studied, large uncertainties remain due to methodological constraints. To address this problem, we proposed a new method that enables the separation of autotrophic respiration (Ra) from ecosystem respiration (Re) by assuming that Ra is related to the lower bound of the relationship between Re and GPP. By applying this method, we analyzed flux data acquired from 195 sites globally in an investigation of spatiotemporal dynamics in CUE. The results revealed a global average CUE of 0.50 ± 0.13, with the greatest values corresponding with croplands and the lowest with mixed forests. Spatially, CUE was greatest for Mediterranean and subtropical regions, and least for tropical regions. Temporally, CUE exhibited seasonal fluctuations across most biomes, with CUE increasing during the early growing season and then decreasing as the season progressed. We also investigated CUE's response to variations in several environmental drivers (e.g., air temperature, soil moisture, and incident solar radiation), with the help of machine learning, specifically extreme gradient boosting (xgboost) and a SHapley Additive exPlanation (SHAP)-value based interpretation of the results. A negative relationship was shown to exist between ambient CO2 concentrations and CUE, confirming hypotheses that relate translocation and accumulation of nonstructural carbohydrates in plant tissues. These findings highlight the feasibility and value of leveraging flux data through advanced methods in deepening our understanding of CUE dynamics and their regulation at a global scale.

中文翻译：

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从全球陆地生物群落的涡度相关测量得出的碳利用效率的时空变化


植被碳利用效率 （CUE） 是净初级生产力 （NPP） 和总初级生产力 （GPP） 之间的比率，有助于深入了解生态系统将大量碳 （C） 从大气转移到潜在 C 汇的能力。尽管之前已经研究了 CUE 对气候变化的模式和反馈，但由于方法论的限制，仍然存在很大的不确定性。为了解决这个问题，我们提出了一种新方法，通过假设 Ra 与 Re 和 GPP 之间关系的下限相关，可以将自养呼吸 （Ra） 与生态系统呼吸 （Re） 分开。通过应用这种方法，我们分析了从全球 195 个站点获得的通量数据，以调查 CUE 中的时空动力学。结果显示，全球平均 CUE 为 0.50 ± 0.13，最大值对应于农田，最低对应于混交林。在空间上，地中海和亚热带地区的 CUE 最大，而热带地区的 CUE 最小。从时间上讲，CUE 在大多数生物群落中表现出季节性波动，CUE 在生长季节的早期增加，然后随着季节的进行而降低。我们还在机器学习的帮助下，特别是极端梯度提升 （xgboost） 和基于 SHapley 加法解释 （SHAP） 值的结果解释，研究了 CUE 对几个环境驱动因素（例如，气温、土壤湿度和入射太阳辐射）变化的响应。环境 CO2 浓度与 CUE 之间存在负相关关系，证实了将植物组织中非结构性碳水化合物的易位和积累联系起来的假设。 这些发现强调了通过先进方法利用通量数据以加深我们对全球范围内 CUE 动力学及其调节的理解的可行性和价值。