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

Leaf dark respiration (R), 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), 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 variability and its associations with V 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 than commonly used traits like LMA, N and P concentrations, but univariate trait-R relationships were always weak (r ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-R relationships, accurately predicted cross-site R (r = 0.65) and pinpointed the factors contributing to R variability. Our findings reveal a few novel traits with greater cross-site scalability regarding R, challenging the use of empirical trait-R relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating R, which could ultimately improve process modeling of terrestrial plant respiration.