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Abstract Amazônia is a species-rich region of immense importance to Earth’s water and carbon cycling. Photosynthesis drives the global carbon cycle, so understanding photosynthetic differences across diverse landscapes is a key task of ecophysiology and ecosystem science. Unfortunately, due to physiological and logistical constraints, ground-based photosynthesis data in Amazônia remain scarce and the ‘traditional’ steady-state (SS) method of gas exchange is slow and inefficient. The Dynamic Assimilation™ Technique (DAT) promises a new way to perform A/Ci curves rapidly without requiring SS conditions. Thus far, this technique has only been validated in greenhouse or agricultural-field-grown species and has yet to be tested in forest trees of diverse physiology morphology and environmental adaptation. To test the utility of the DAT in a complex tropical forest ecosystem, we compared the DAT with the SS method in 13 Amazonian trees in situ. We found strong agreement between Vcmax from DAT curves and SS curves, while Jmax was underestimated in DAT curves. We conclude that the DAT provides a robust and rapid estimation of Vcmax. We also identified diverse and unexpected DAT curve shapes among some trees, including the presence of an ‘overshoot’ in assimilation beyond model-derived ribulose-1,5-bisphosphate (RuBP) regeneration limitations. The presence of overshoot may elucidate microclimate and species differences in RuBP regeneration rates and emphasizes the considerable importance of DAT curve protocol specifications, such as the effect of ramp rate and direction on Jmax and TPU. Overall, the DAT saved time relative to the SS method and proved to be an effective and rapid method for quantifying Vcmax in tropical trees. 

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 agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development 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 capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/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.