Deriving optimal air temperature from light use efficiency to improve ecosystem productivity estimates

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ID: 319300
2026
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Abstract
Abstract Accurately representing photosynthetic optimum temperature (Topt) is essential for predicting terrestrial carbon uptake, yet conventional ecosystem-scale estimates based on peak gross primary productivity (GPP) are confounded by concurrent variations in radiation, moisture and phenology. Here, we develop a physiologically grounded approach that defines Topt as the air temperature where light use efficiency (LUE) reaches its maximum, thereby isolating the intrinsic thermal response of photosynthesis. We derived efficiency-based Topt (Topt-LUE) from 131 flux observation sites. By replacing biome-based Topt used in Vegetation Photosynthesis Model (VPM), we significantly improved GPP estimation (R2 = 0.71, RMSE = 1.93 gC m−2 d−1) compared to the biome-based approach (R2 = 0.62, RMSE = 2.84 gC m−2 d−1). We then used a Random Forest framework to generate global and time-varying Topt-LUE fields (2001–2020). The results revealed that biome-based Topt systematically overestimated Topt-LUE across ∼94% of global vegetated areas, with a mean bias of ∼10°C. The global Topt-LUE exhibit clear latitudinal gradients and biome-specific contrasts, providing evidence for widespread thermal acclimation of ecosystem photosynthesis. This acclimation is reflected in a mean increase in Topt-LUE of 0.021 ± 0.102 °C per year, underscoring a measurable response to long-term climate changes. When integrated into VPM, the dynamic Topt-LUE fields reshape the spatial pattern of simulated carbon uptake, mitigating overestimation in tropical areas (∼5 gC m−2 d−1) and enhancing underestimation in frigid areas and temperate regions including China, India and Europe. This study established a mechanistically grounded framework for quantifying ecosystem thermal acclimation, advancing the representation of temperature responses in terrestrial carbon cycle models.
Reference Key
openalex_W7166884335 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Suning Chen, Mingchun Wu, Peilin Wang, Yaojie Liu, Linsheng Wu, Yongguang Zhang, Zhaoying Zhang
Journal journal of plant ecology
Year 2026
DOI
10.1093/jpe/rtag153
URL
https://doi.org/10.1093/jpe/rtag153
Keywords Keywords not found

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