Predicting Forest Respiration Rates

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Recently, a team of researchers, led by Zhengbing Yan of the Chinese Academy of Sciences, explored how to improve prediction of leaf dark respiration (R_dark), which is a key but under-measured component of forest carbon cycling. This study, which was published in the journal New Phytologist, addresses the issue of traditional leaf trait–based predictions falling short across different forest types by presenting a new solution that is more accurate and scalable (1).

Why is leaf dark respiration important?

Dark respiration measures leaf respiration when light is not present (2). This metabolic process plays an important role in regulating global carbon dynamics (1,2). Currently, most terrestrial biosphere models estimate R_dark using leaf traits such as maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), and concentrations of nitrogen (N) and phosphorus (P) (1). Until now, the reliability of these trait-based relationships across diverse forest ecosystems, including temperate, subtropical, and tropical, has been largely untested.

What did the researchers do in their study?

In their study, the researchers conducted a field study across three major forest types in China to evaluate how R_dark varies and which leaf characteristics best explain that variability. To their surprise, the researchers found LMA, nitrogen, and phosphorus were weak indicators of R_dark, with univariate relationships showing low explanatory power (r² ≤ 0.15) (1). Instead, the study identified magnesium and calcium concentrations as more influential across sites. Even so, these relationships remained forest-specific, limiting their usefulness in broad-scale models (1).

Applying leaf spectroscopy allowed the researchers to predict R_dark with improved accuracy. Spectroscopy not only captured variability more effectively but also revealed which underlying factors were driving differences in R_dark, which are insights that conventional trait measurements struggled to provide (1).

What is the key takeaway from this study?

The main takeaway is that the scalability of spectroscopy shows its potential as an improved monitoring tool that could lead to major improvements in simulations of plant respiration. In turn, this could help improve global carbon cycle predictions moving forward.

References

1. Wu, F.; Liu, S.; Lamour, J.; et al. Linking Leaf Dark Respiration to Leaf Traits and Reflectance Spectroscopy Across Diverse Forest Types. New Phytol. 2025, 246 (2), 481–497. DOI: 10.1111/nph.20267
2. Fonseca, J. P.; Griffiths, M.; York, L. M.; Mysore, K. S. Dark Respiration Measurement from Arabidopsis Shoots. Bio. Protoc. 2021, 11 (19), e4181. DOI: 10.21769/BioProtoc.4181