Plant water potential improves prediction of empirical stomatal models

Abstract

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.

Fig 1. Partial dependency of stomatal conductance (g_s) on measured leaf water potential (ψ_leaf) in (a) temperate gymnosperm species, (b) temperate angiosperm species, (c) tropical deciduous species, and (d) tropical evergreen species.

Y-axis values are scaled from 0–1 by the percentage of stomatal closure reached. Species codes are the first two letters of the genus and the first two letters of the species from Table A in S1 File.

Fig 2

(a) Relationship across species between the water potential at which 50% of stomatal conductance is lost (ψ_gs50) and the water potential at which 50% of stem hydraulic conductance (ψ_x50) is lost. Black line is the 1:1 line and red the OLR regression best fit. (b) Histogram of the percent loss of stem hydraulic conductance (PLC) at the water potential at which 50% of stomatal conductance is lost (ψ_gs50).

Fig 3

(a) Akaike Information Criterion (AIC) value average across all 24 species for the Medlyn (M), Ball-Berry-Leuning (BBL), Ball-Berry-Leuning plus Hydraulics (BBL.H), and Tuzet (T) stomatal conductance models. (b) Frequency of species’ delta-AIC values between the BBL and M models. (c) Frequency of species’ delta-AIC values between the BBLH and M models. (d) Frequency of species’ delta-AIC values between the T and M models. In each of (b-d), the top number is the mean delta-AIC across all species and the lower number is the median delta-AIC.

Fig 4

(a) Root mean squared error (RMSE) and (e) OLS regression R² between predicted and observed stomatal conductance averaged across all 24 species for the Medlyn (M), Ball-Berry-Leuning (BBL), Ball-Berry-Leuning plus Hydraulics (BBL.H), and Tuzet (T) stomatal conductance models. (b and f) Frequency of species’ % improvement in RMSE and R² respectively between the BBL and M models. (c and g) Frequency of species’ % improvement in RMSE and R² respectively between the BBLH and M models. (d and h) Frequency of species’ % improvement in RMSE and R² respectively between the T and M models.

Fig 5. (a) Slope between the residuals of observed versus predicted stomatal conductance and leaf temperature (Tleaf), (b) vapor pressure deficit (VPD), and (c) soil water potential (PsiS).

Negative values in the Tleaf and VPD plots indicate over-prediction of g_s during dry conditions (high Tleaf and high VPD), whereas positive values in the PsiS plot indicates over-prediction of g_s during dry conditions. Models are Medlyn (red), Ball-Berry-Leuning (darkred), Ball-Berry-Leuning plus the hydraulic term (blue) and Tuzet (green). Error bars are +/- 1 S.E.

Fig 6. Histogram of species’ percent bias in prediction of stomatal conductance at the 10^th percentile of soil water potential.

Negative numbers mean over-prediction of g_s by models compared to measured values. Solid line is the mean across species and dashed line the median. Models are (a) Medlyn model, (b) Ball-Berry-Leuning model, (c) Ball-Berry-Leuning plus Hydraulics model, and (d) Tuzet model.