Data from: Plant water potential improves prediction of empirical stomatal models

  • Brendan Choat (Contributor)
  • Brett T. Wolfe (Contributor)
  • W. R L Anderegg (Contributor)
  • Frederick Meinzer (Contributor)
  • S. Pacala (Contributor)
  • VĂ­ctor Resco de Dios (Contributor)
  • A. Wolf (Contributor)
  • Pilar Pita (Contributor)
  • John Sperry (Contributor)
  • Thomas Kolb (Northern Arizona University) (Contributor)
  • Shan Li (Contributor)
  • Daniel J. Chmura (Contributor)
  • Steven Jansen (Contributor)
  • Adriana Arango-Velez (Contributor)

Dataset

Description

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.,AllData_PLoSONE_SI_2017_v1,
Date made availableOct 1 2017
PublisherDRYAD

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