Yan Yulin, Li Bolun, Dechant Benjamin, Xu Mingzhu, Luo Xiangzhong, Qu Sai, Miao Guofang, Leng Jiye, Shang Rong, Shu Lei, Jiang Chongya, Wang Han, Jeong Sujong, Ryu Youngryel, Chen Jing M
Geography Postdoctoral Program, Fujian Normal University, Fuzhou, China.
Key Laboratory of Humid Subtropical Eco-Geographical Process (Ministry of Education), Fujian Normal University, Fuzhou, China.
Nat Plants. 2025 Apr;11(4):924-934. doi: 10.1038/s41477-025-01958-2. Epub 2025 Mar 25.
Photosynthetic efficiency (PE) quantifies the fraction of absorbed light used in photochemistry to produce chemical energy during photosynthesis and is essential for understanding ecosystem productivity and the global carbon cycle, particularly under conditions of vegetation stress. However, nearly 60% of the global spatiotemporal variance in terrestrial PE remains unexplained. Here we integrate remote sensing and eco-evolutionary optimality theory to derive key plant traits, alongside explainable machine learning and global eddy covariance observations, to uncover the drivers of daily PE variations. Incorporating plant traits into our model increases the explained daily PE variance from 36% to 80% for C vegetation and from 54% to 84% for C vegetation compared with using climate data alone. Key plant traits-including chlorophyll content, leaf longevity and leaf mass per area-consistently emerge as important factors across global biomes and temporal scales. Water availability and light conditions are also critical in regulating PE, underscoring the need for an integrative approach that combines plant traits with climatic factors. Overall, our findings demonstrate the potential of remote sensing and eco-evolutionary optimality theory to capture principal PE drivers, offering valuable tools for more accurately predicting ecosystem productivity and improving Earth system models under climate change.
光合效率（PE）量化了光合作用过程中用于光化学反应以产生化学能的吸收光的比例，对于理解生态系统生产力和全球碳循环至关重要，特别是在植被胁迫条件下。然而，陆地光合效率近60%的全球时空变化仍无法解释。在这里，我们整合了遥感和生态进化最优性理论，以推导关键植物性状，并结合可解释的机器学习和全球涡度协方差观测，来揭示每日光合效率变化的驱动因素。与仅使用气候数据相比，将植物性状纳入我们的模型后，C3植被的每日光合效率可解释方差从36%提高到80%，C4植被从54%提高到84%。关键植物性状，包括叶绿素含量、叶片寿命和单位面积叶质量，在全球生物群落和时间尺度上始终是重要因素。水分可用性和光照条件在调节光合效率方面也很关键，这凸显了将植物性状与气候因素相结合的综合方法的必要性。总体而言，我们的研究结果证明了遥感和生态进化最优性理论捕捉光合效率主要驱动因素的潜力，为更准确地预测生态系统生产力和改进气候变化下的地球系统模型提供了有价值的工具。
