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  1. Niu, Shuli (Ed.)
  2. Abstract

    Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe howAresponds to changes in light and atmospheric CO2concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi‐hypothesis methods (that account for both hypothesis and parameter variability) for process‐level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting‐rate selection. Each of the four processes comprises 1–3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high‐resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influentialparametersbut also reveal the surprising and marked dominance of the limiting‐rate selectionprocess(accounting for 57% of the variation inAvs. 22% for carboxylation). The limiting‐rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reducesAbelow the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation onA. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in globalAby 4%–10%, equivalent to 50%–160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi‐hypothesis methods.

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  3. null (Ed.)
  4. Summary

    Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (Vc,max) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predictingVc,maxis still lacking.

    Here the ability of leaf spectroscopy for rapid estimation ofVc,maxwas tested.Vc,maxwas estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predictVc,maxfrom leaf spectra.

    The results demonstrated that leaf spectroscopy accurately predictsVc,maxof mature leaves in Panamanian tropical forests (R2 = 0.90). However, this single‐age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models forVc,maxand leaf age enabled construction of theVc,max–age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability inVc,max, assuming sufficient sampling across diverse species, leaf ages and canopy environments.

    This finding will aid development of remote sensing approaches that can be used to characterizeVc,maxin moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

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  5. 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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