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Summary Leaf dark respiration (Rdark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (Vcmax), 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 analyzedRdarkvariability and its associations withVcmaxand 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‐siteRdarkthan commonly used traits like LMA, N and P concentrations, but univariate trait–Rdarkrelationships were always weak (r2 ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–Rdarkrelationships, accurately predicted cross‐siteRdark(r2 = 0.65) and pinpointed the factors contributing toRdarkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingRdark, challenging the use of empirical trait–Rdarkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingRdark, which could ultimately improve process modeling of terrestrial plant respiration.
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From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance
Summary Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long‐standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking.We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m−2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needleleaf species, and upper‐ and lower‐canopy (i.e. sun and shade) growth environments.Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2 = 0.89; root mean square error (RMSE) = 15.45 g m−2).Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.
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- Award ID(s):
- 1638720
- PAR ID:
- 10375691
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 224
- Issue:
- 4
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 1557-1568
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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