November 19, 2024
Unveiling the Drivers of Leaf Respiration Across Forest Types
Leaf spectroscopy offers a promising tool to predict leaf respiration and its relationship with leaf traits.

The three forest sites span a large latitudinal gradient.
[Reprinted with permission from Wu, F., et al. "Linking Leaf Dark Respiration to Leaf Traits and Reflectance Spectroscopy Across Diverse Forest Types." New Phytologist Early View (2024). DOI:10.1111/nph.20267.]
The Science
Leaf dark respiration (Rdark) is a key process in global carbon cycles. This study measured Rdark, leaf reflectance, and other leaf traits across three forest types in China. Spectroscopy out performed leaf trait–Rdark relationships as a tool for predicting Rdark.
The Impact
This research provides valuable insights into the factors controlling Rdark, a critical component of plant metabolism and ecosystem carbon balance. The strong performance of leaf spectroscopy in predicting Rdark offers a rapid and efficient alternative to traditional, time-consuming measurements. This could significantly improve monitoring and modeling plant respiration at larger scales, leading to more accurate predictions of carbon fluxes in terrestrial ecosystems.
Summary
This study investigated how Rdark normalized to 25°C (Rdark25) relates to leaf traits and spectral reflectance across three diverse forest types in China. The research was conducted at a temperate forest in Changbai Mountain, a subtropical forest in Gutian Mountain, and a tropical forest in Xishuangbanna. Researchers found that while Rdark25 differed across the three forest types, the ratio of Rdark25 to maximum carboxylation capacity normalized to 25°C (Vcmax25) also varied significantly. This finding suggests the commonly used constant ratio in some terrestrial biosphere models might not accurately reflect real-world variation and may lead to underestimations of Rdark.
When analyzing the relationship between Rdark25 and leaf traits, the study found magnesium and calcium concentrations were most strongly linked to Rdark25 variation. However, all univariate trait–Rdark25 relationships were weak (r2 ≤ 0.13) and forest-type specific, suggesting no single universal scaling relationship exists to predict Rdark25 across broad geographic scales. The study found leaf reflectance spectroscopy provided a more robust and efficient method for predicting Rdark25 across forest types compared to models based on leaf traits. The “cross-site” spectral model, which used data from all three sites, showed the highest accuracy (r2 = 0.65), highlighting the importance of including a wide range of spectral and trait variability for building generalizable models. The study identified key spectral bands related to pigments, leaf structure, and water content that contribute to Rdark25 prediction, further indicating the potential of spectroscopy for monitoring multiple leaf traits. The team recommends expanding this research to include a broader range of plant species and ecosystems, investigating seasonal and vertical variations in Rdark25, and exploring the use of imaging spectroscopy for monitoring Rdark at larger scales.
Principal Investigator
Zhengbing Yan
Institute of Botany, Chinese Academy of Sciences
[email protected]
Program Manager
Brian Benscoter
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]
Funding
This work was supported by the Key Talent Project of the State Key Laboratory of Vegetation and Environmental Change (LVEC-2023rc01). Support was received from the National Natural Science Foundation of China (32471573), the National Natural Science Foundation of China (32401379), an Australian Research Council Discovery Project grant (DP220101882), and the NASA Surface Biology and Geology Mission. Funding was also received from the Next-Generation Ecosystem Experiments Tropics project that is funded by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research program and by DOE to Lawrence Berkeley National Laboratory (contract no. DE-AC02-05CH11231).
References
Wu, F., et al. "Linking Leaf Dark Respiration to Leaf Traits and Reflectance Spectroscopy Across Diverse Forest Types." New Phytologist Early View (2024). https://doi.org/10.1111/nph.20267.