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

Leaf dark respiration (R<sub>dark</sub>), 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<sub>cmax</sub>), 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<sub>dark</sub> variability and its associations with V<sub>cmax</sub> 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<sub>dark</sub> than commonly used traits like LMA, N and P concentrations, but univariate trait-R<sub>dark</sub> relationships were always weak (r<sup>2</sup> ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-R<sub>dark</sub> relationships, accurately predicted cross-site R<sub>dark</sub> (r<sup>2</sup> = 0.65) and pinpointed the factors contributing to R<sub>dark</sub> variability. Our findings reveal a few novel traits with greater cross-site scalability regarding R<sub>dark</sub>, challenging the use of empirical trait-R<sub>dark</sub> relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating R<sub>dark</sub>, which could ultimately improve process modeling of terrestrial plant respiration.

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Publication Details

Journal
New Phytologist
Published
2024-11-19
DOI
https://doi.org/10.1111/nph.20267
Citations
9
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
4.13

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article

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

Julien Lamour, Nicholas G. Smith, Aijun Xing, Nathan G. Swenson et al.
9 citations
New Phytologist
Remote Sensing in Agriculture
4.13
article

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

Julien Lamour, Nicholas G. Smith, Aijun Xing, Nathan G. Swenson, Nan Yang, Jin Wu, Owen K. Atkin, Shuwen Liu, Zhengbing Yan, Peter B. Reich, Yue Shi, Fengqi Wu, Alistair Rogers, Shawn Paul Serbin, Han Wang, Tingting Dong, Jens Kattge, Yanjun Su, Xiaojuan Liu, Zhihui Wang, Jinlong Dong, Guanhua Dai, Weiying Xu
article en
9 citations

Abstract

Leaf dark respiration (R<sub>dark</sub>), 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<sub>cmax</sub>), 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<sub>dark</sub> variability and its associations with V<sub>cmax</sub> 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<sub>dark</sub> than commonly used traits like LMA, N and P concentrations, but univariate trait-R<sub>dark</sub> relationships were always weak (r<sup>2</sup> ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-R<sub>dark</sub> relationships, accurately predicted cross-site R<sub>dark</sub> (r<sup>2</sup> = 0.65) and pinpointed the factors contributing to R<sub>dark</sub> variability. Our findings reveal a few novel traits with greater cross-site scalability regarding R<sub>dark</sub>, challenging the use of empirical trait-R<sub>dark</sub> relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating R<sub>dark</sub>, which could ultimately improve process modeling of terrestrial plant respiration.

New PhytologistVol. 246(2)
University of Notre Dame (US), Australian National University (AU), Texas Tech University (US), Centre National de la Recherche Scientifique (FR), University of Minnesota (US), Goddard Space Flight Center (US), Minnesota Department of Natural Resources (US), Université Toulouse III - Paul Sabatier (FR), Lawrence Berkeley National Laboratory (US), Université Fédérale de Toulouse Midi-Pyrénées (FR), Chinese Academy of Sciences (CN), University of Michigan (US), Beijing Botanical Garden (CN), German Centre for Integrative Biodiversity Research (DE), Xishuangbanna Tropical Botanical Garden (CN), Guangdong Academy of Sciences (CN), Guangzhou Institute of Geography (CN), Institute of Forest Ecology, Environment and Protection (CN), Max Planck Institute for Biogeochemistry (DE), Institute of Botany (CN), University of Chinese Academy of Sciences (CN), Institut de Recherche pour le Développement (FR), China National Botanical Garden, Western Sydney University (AU), University of Hong Kong (HK), Tsinghua University (CN)
U.S. Department of Energy, National Aeronautics and Space Administration, National Natural Science Foundation of China, Western Sydney University, Office of Science, Australian Research Council, Biological and Environmental Research, Lawrence Berkeley National Laboratory
Life in Land
Openalex Percentile: Top 6%
Remote Sensing in Agriculture
4.13
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