The stomatal response to rising CO2 concentration and drought

The stomatal response to rising CO2 concentration and drought is predicted by a hydraulic trait-based optimization model

by Wang Y., Sperry J. S., Venturas M. D., Trugman A. T., Love D. M., Anderegg W. R. L. (2019)

Yujie Wang 1, John S. Sperry1, Martin D. Venturas 1, Anna T. Trugman 1, David M. Love 1,2 and William R. L. Anderegg 1


1 School of Biological Sciences, University of Utah, Salt Lake City, 257S 1400E, UT 84112, USA;

2 Warnell School of Forestry and Natural Resources, University of Georgia, 180 E Green Street, Athens, GA 30602-2152, USA

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In Tree Physiology 00: 1-12 – https://doi.org/10.1093/treephys/tpz038

http://sperry.biology.utah.edu/publications/Wang_et_al_2019_TPH.pdf

Abstract

Modeling stomatal control is critical for predicting forest responses to the changing environment and hence the global
water and carbon cycles. A trait-based stomatal control model that optimizes carbon gain while avoiding hydraulic
risk has been shown to perform well in response to drought. However, the model’s performance against changes in
atmospheric CO2, which is rising rapidly due to human emissions, has yet to be evaluated. The present study tested
the gain–risk model’s ability to predict the stomatal response to CO2 concentration with potted water birch (Betula
occidentalis Hook.) saplings in a growth chamber. The model’s performance in predicting stomatal response to changes
in atmospheric relative humidity and soil moisture was also assessed. The gain–risk model predicted the photosynthetic
assimilation, transpiration rate and leaf xylem pressure under different CO2 concentrations, having a mean absolute
percentage error (MAPE) of 25%. The model also predicted the responses to relative humidity and soil drought with
a MAPE of 21.9% and 41.9%, respectively. Overall, the gain–risk model had an MAPE of 26.8% compared with the
37.5% MAPE obtained by a standard empirical model of stomatal conductance. Importantly, unlike empirical models,
the optimization model relies on measurable physiological traits as inputs and performs well in predicting responses
to novel environmental conditions without empirical corrections. Incorporating the optimization model in larger scale
models has the potential for improving the simulation of water and carbon cycles.

Prediction of stomatal responses to climatic water deficits

 

 

Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits

Sperry J. S., Wang Y., Wolfe B. T., Mackay D. S., Anderegg W. R. L., McDowell N. G., Pockman W. T. (2016) 

New Phytologist

Early View (Online Version of Record published before inclusion in an issue)

Summary

  • Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a ‘supply–demand’ theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying.
  • The theory was implemented in a trait-based soil–plant–atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (Pcanopy) from soil water potential (Psoil) and vapor pressure deficit (D).
  • Modeled responses to D and Psoil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and Pcanopy across nine data sets from seasonally dry tropical forest and piñon–juniper woodland with < 26% mean error.
  • Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root–stem–leaf continuum will simplify parameterization.