植物性状塑造全球光合效率时空变化

报告人：鄢予霖 博士

日期：2025年5月15日

腾讯会议号：400169970

时间：上午10:00-11:00

报告内容简介

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 C3 vegetation and from 54% to 84% for C4 vegetation compared to using climate data alone. Key plant traits—including chlorophyll content, leaf longevity, and leaf mass per area—consistently emerge as valuable 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 predict ecosystem productivity and improving Earth system models under climate change.

报告人简介

鄢予霖，2022年获首尔大学农林气象学博士学位，现为福建师范大学博士后，主要从事生态系统功能特性、陆地生态系统碳水通量以及可持续信息农业等研究。熟悉宏观农作物遥感制图，叶片植物生理及观测，作物生长模型，常规机器学习方法，以及大尺度碳水通量机理模型。主要研究成果发表于Nature Plants, Plant, Cell & Environment, Agricultural and Forest Meteorology, ISPRS Journal of Photogrammetry and Remote Sensing等期刊。