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The effect of the vertical gradients of photosynthetic parameters on the CO 2 assimilation and transpiration of a Panamanian tropical forest.

Julien LamourKenneth J DavidsonKim S ElyGilles Le MoguédecJeremiah A AndersonQianyu LiOsvaldo CalderónCharles D KovenS Joseph WrightAnthony P WalkerShawn P SerbinAlistair Rogers
Published in: The New phytologist (2023)
Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama, and then quantified the impact of the observed gradients on simulated canopy scale CO 2 and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy scale simulations of water vapor and CO 2 exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.
Keyphrases
  • climate change
  • genome wide
  • machine learning
  • gene expression
  • deep learning
  • artificial intelligence
  • neural network