光合作用
适应
大气科学
涡度相关法
生态系统
生物圈
天蓬
植物
环境科学
生物
物理
生态学
作者
Xiangzhong Luo,Trevor F. Keenan
标识
DOI:10.1038/s41559-020-1258-7
摘要
Photosynthesis responds quickly to changes in light, increasing with incoming photosynthetic photon flux density (PPFD) until the leaves become light saturated. This instantaneous response to PPFD, which is widely studied and incorporated into models of photosynthesis, is overlaid on non-instantaneous photosynthetic changes resulting from the acclimation of plants to average PPFD over intermediate timescales of a week to months $$\left( {\overline {{\mathrm{PPFD}}} } \right)$$ . Such photosynthetic light acclimation is not typically incorporated into models, due to the lack of observational constraints. Here, we use eddy covariance observations from globally distributed and automated sensor networks, along with photosynthesis estimates from nine terrestrial biosphere models (TBMs), to quantify and assess photosynthetic acclimation to light in natural environments. We also use recent theoretical developments to incorporate light acclimation in a TBM. Our results show widespread light acclimation of ecosystem photosynthesis. On average, a 1 μmol m−2 s−1 increase in $$\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}$$ (ten-day average PPFD) leads to a 0.031 ± 0.013 μmol C m−2 s−1 increase in the maximum photosynthetic assimilation rate (Amax), with croplands having stronger acclimation rates than grasslands and forests. Our analysis shows that the TBMs examined either neglect or substantially underestimate light acclimation. By updating a TBM to include photosynthetic acclimation, successfully reproducing the $$\overline {{\mathrm{PPFD}}} _{{\mathrm{10}}}$$ –Amax relationship, we provide a robust method for the incorporation of photosynthetic light acclimation in future models. Combining global eddy covariance observations and photosynthesis estimates from terrestrial biosphere models, the authors demonstrate widespread acclimation of photosynthesis to light in natural environments, with croplands showing stronger acclimation rates than forests or grasslands.
科研通智能强力驱动
Strongly Powered by AbleSci AI