Linking leaf dark respiration to leaf traits and reflectance spectroscopy across diverse forest types

Abstract

• Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.
• Here, we analyzed R_dark variability and its associations with V_cmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.
• We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site R_dark than commonly used traits like LMA, N and P concentrations, but univariate trait–R_dark relationships were always weak (r² ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_dark relationships, accurately predicted cross-site R_dark (r² = 0.65) and pinpointed the factors contributing to R_dark variability.
• Our findings reveal a few novel traits with greater cross-site scalability regarding R_dark, challenging the use of empirical trait–R_dark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating R_dark, which could ultimately improve process modeling of terrestrial plant respiration.