the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 May 2025
The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
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Status: open (until 11 Aug 2025)
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RC1: 'Comment on essd-2025-213', Anonymous Referee #1, 18 Jun 2025
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Reviewer Report: Manuscript essd-2025-213
Title: The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity
Major Strengths
High Scientific Value: Establishes the first open-access global database (GSTI) systematically integrating leaf spectroscopy and photosynthetic functional traits, addressing a critical data gap for cross-species/environment spectral model development.
Methodological Standardization: Provides unified data processing workflows (R scripts) and parameter-fitting standards (e.g., FvCB model), ensuring data comparability and reproducibility.
Exceptional Data Scale: Covers 41 sites, 397 species, and >7,500 observations—significantly exceeding existing similar efforts.
FAIR Compliance: Open data (GitHub/ESS-Dive) under CC-BY 4.0 aligns with modern scientific data-sharing practices.
Required Revisions: Scientific and Logical Issues
- Contradiction in Methodological Description
Issue (Page 10):
Original text: "We considered the mesophyll conductance infinite, therefore estimates of Vcmax25,Jmax25 and are ‘apparent’ values..."
Problem: The phrasing "considered" inaccurately implies an active choice, while the FvCB model inherently assumes infinite gm.
Revision:"The FvCB model intrinsically assumes infinite mesophyll conductance; thus, estimated parameters represent apparent values based on intercellular CO₂ concentration (Ci)."
- Inadequate Discussion of Data Representation Bias
Issue (Pages 14–15, Fig. 5):
Data for temperate coniferous forests are minimal (32 observations, no full-range spectra), yet the text claims coverage of "temperate mixed broadleaf forests" without highlighting this gap.
Africa is entirely unrepresented (e.g., savannas comprise ~11% of global vegetated area), potentially limiting model generalizability.
Revision: In Section 4.1 ("Data coverage"), quantify ecological significance of underrepresented biomes (e.g., African savannas, Mediterranean ecosystems) and assess impacts on model robustness.- Ambiguous Model Validation Protocol
Issue (Page 12):
PLSR validation uses "random selection of 80% for training and 20% for validation" but omits whether sampling was stratified by dataset. Global randomization risks data leakage if samples from the same dataset appear in both training/validation sets.
Revision: Clarify the sampling strategy (e.g., "stratified random sampling by source dataset") to prevent overestimation of model performance.- Missing Figure Citations
Issue (Page 16):
The description of trait correlations ("Figure 7 illustrates...") lacks a formal figure citation.
Revision: Insert "Figure 7" when first referenced:"Figure 7 illustrates bivariate relationships between Vcmax25 and other traits..."
Language and Presentation Errors
Inconsistent Terminology (Page 6 vs. Table 1):
Text uses "leaf mass per area (LMA)", but Table 1 abbreviates it as "ALM".
Correction: Standardize to "LMA" throughout.Ambiguous Units (Table 1):
"Wave_XX: Reflectance at wavelength XX, percent"
Correction: Specify as "Reflectance (fraction, 0–1)" or "Reflectance (%, 0–100)".Incorrect Subscript (Fig. 6 Caption):
"TPUs" → Correction: Use "TPU25" (consistent with text).
Typographical Error (Abstract):
"agricultrual" → Correction: "agricultural".
Additional Recommendations
Data Quality Control: Expand Section 2.2.6 to describe outlier handling (e.g., exclusion criteria) when f.Check_data() flags values outside expected ranges.
Roadmap for C4/CAM Data: In Section 4.1, specify plans/timelines to incorporate C4/CAM species (e.g., collaborations in progress).
Citation Updates: Replace preprint citations (e.g., Luo et al., 2024) with peer-reviewed versions where available, or label as "in review/preprint".
Decision
This work presents a valuable contribution to plant spectroscopy and functional ecology. However, revisions are required to address methodological clarity, data representativeness, and presentation consistency.
Recommendation: Minor RevisionSincerely,
Invited Reviewer, ESSD
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RC2: 'Comment on essd-2025-213', Anonymous Referee #2, 29 Jun 2025
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This GSTI dataset covers a diverse range of environmental conditions and species, allowing the development of robust modeling approaches. It makes a significant contribution to the scientific community. The discussion is also very informative, effectively highlighting existing gaps and potential areas for future improvements. Only a few major issues:
Table 1. Are there any environment-related variables available, such as local weather data associated with the selected plants? These variables could provide valuable insights into environmental influences.
Line 230: is it possible to create a figure showing the three data processing steps?
Figure 7. Since the purpose is to show correlation, using correlation coefficient (e.g., R) instead of R^2 is better. In addition, RMSE should have units.
Line 375: ‘RMSE values were below 10%’ might be a typo, RMSE has units and not in a percentage base.Citation: https://doi.org/10.5194/essd-2025-213-RC2
Data sets
The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérome Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao https://github.com/plantphys/gsti
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