Extrapolating Physiological Response to Drought through Step-by-Step Analysis of Water Potential

Water potential (Ψ) is defined as the energy required to move water between the different compartments of a closed system. In plants, water flows passively in the direction of decreasing Ψ, from slightly negative Ψ at the soil and root level to strongly negative Ψ at the leaf level. The water deficit of the atmosphere is the driving force of water flow through the change in physical state (from liquid to vapor) that occurs at the leaf level. Such an atmospheric pump allows water to circulate along the soil-plant-atmosphere continuum, from the soil to the leaves and, ultimately, to the stomatal chamber through the xylem conducting elements. The upward movement of water via the roots, trunk, branches, and leaves is controlled by two complementary phenomena: cohesion and tension (Dixon and Joly, 1895). The vaporization of water in the stomata creates a negative hydrostatic pressure (or tension) that propagates along the water column due to the cohesion between water molecules (mainly due to hydrogen bonds). As the amount of water supplied by the root system and transported through the xylem cannot compensate for the transpired water, daily patterns in Ψ_leaf are observed, with minimum values when transpiration is at its maximum level (12:00–14:00 solar time, i.e. Ψ at midday [Ψ_md]). At nighttime, transpiration ceases, the water diffuses passively along the water column, and the gradient of Ψ disappears. Ψ at predawn (Ψ_pd) thus represents the water potential of the soil prospected by the root system (Améglio et al., 1999).

The difference between Ψ_pd and Ψ_md is related to the regulation of plant water status in response to soil water content and, thus, to Ψ_pd. The correlation between Ψ_pd and Ψ_md has been used to describe the stringency of water regulation, notably through stomatal opening, root, stem, and leaf hydraulic conductance, and stem capacitance (Martínez-Vilalta et al., 2014). The slope of the linear regression between Ψ_pd and Ψ_md describes the relative sensitivity of the transpiration rate and plant hydraulic conductance to declining water availability. Two typical patterns, namely isohydricity and anisohydricity, have been described depending on the value of this parameter (Martínez-Vilalta et al., 2014). A slope equal to 1 indicates that plant transpiration is proportional to the hydraulic conductance, maintaining a similar daily pressure drop during drought and therefore a low, or no, regulation (anisohydricity). On the contrary, a slope of <1 is observed in tightly regulating, i.e. isohydric, species. In other words, isohydric species should maintain Ψ_md at a higher level than anisohydric species in similar soil water status and/or evaporative demand.

In this issue of Plant Physiology, Knipfer et al. (2020) revisited the correlation between Ψ_pd and Ψ_md using a formula other than simple linear regression, such as the derivative of a smoothed function. Four distinct dry-down experiments including slow (i.e. progressively decreasing irrigation) and rapid (i.e. irrigation stop) drying were carried out in three perennial crops (slow dry-down in almond [Prunus dulcis] and grapevine [Vitis vinifera] and slow and rapid dry-down in walnut [Juglans regia]). They observed that slope is a dynamic value along the intensity of water stress, as previously shown by Charrier et al. (2018) in different grapevine varieties. As the level of stress increases, the behavior shifts from anisohydric (slope ≥1) to partially isohydric (slope <1). Such dynamic behavior is supported by the change in stomatal sensitivity to vapor pressure deficit (VPD) at similar Ψ (Charrier et al., 2018). Assuming a piecewise linear correlation between Ψ_pd and Ψ_md, Knipfer et al. (2020) observed two statistically significant breakpoints (Θ₁ and Θ₂) defining three distinct stages along the water stress gradient. During the first stage, a high transpiration rate can be achieved while the soil is above the saturation point: Ψ_leaf can thus decrease much faster than Ψ_soil, resulting in a slope >1. During the second stage, soil water becomes limiting, and Ψ_leaf reaches values that induce stomatal closure at midday, avoiding large pressure drops at midday, which results in a slope <1. Finally, during the third stage, water loss can no longer be regulated and Ψ_leaf becomes equal to Ψ_soil.

The correlation analysis was supplemented by experimental measurements to identify potential physiological causes for the identified thresholds (Θ₁ and Θ₂). As hypothesized, the first breakpoint (Θ₁) was synchronized with Ψ, inducing 90% stomatal closure (or Ψ_close; Martin StPaul et al., 2017) in the different experiments (fast versus slow drying, and across species). In the first stage of dehydration (above Θ₁), Ψ_leaf is affected by environmental conditions (i.e. light, temperature, and VPD) and does not necessarily reach Ψ_close at midday. However, the identification of Ψ_soil, which is likely to induce stomatal closure and the resulting loss of net assimilation (Θ₁), is a relevant metric for physiologists, agronomists, and foresters. The second breakpoint (Θ₂) was assumed to be the pressure at the turgor loss point (π_TLP). This value should be considered with more caution, as the study reports only relative agreement between Θ₂ and Ψ_TLP. The value for Ψ_TLP was measured in only one experiment using pressure-volume curves (walnut trees during slow dry down), while the other values were taken from the literature. Among species, Ψ_TLP is not necessarily observed at a lower value than Ψ_close (Bartlett et al., 2016), suggesting that the resulting shape of the correlation between Ψ_pd and Ψ_md would be different. An important factor explaining this discrepancy could be the variation in Ψ_TLP among different plant materials (e.g. genotype and phenological and physiological stages). In particular, diurnal variation in nonstructural carbohydrates in leaves can significantly affect osmotic pressure and thus leaf turgor (Charrier, 2020; Gersony et al., 2020). Comparison between breakpoints and other physiological thresholds such as conductance and water content losses in different organ and tissues (e.g. stomata, leaf, petiole, stem, and root; Charrier et al., 2016; Scoffoni et al., 2017; Lamacque et al., 2020) provides a clearer picture of the sequence of events during drought stress. For example, in two grapevine varieties, Θ₁ seems to be more related to the transition from anisohydric to isohydric behavior, whereas Θ₂ would occur near the point of stomatal closure, complete petiole embolism, and loss of leaf conductance and the onset of stem embolism and leaf mortality (Fig. 1). To ensure that the described pattern is generic, different components of plant water regulation should systematically be measured in various species and combined in the promising analytical approach presented by Knipfer et al. (2020).

Figure 1.

A, Relation between Ψ_pd and Ψ_md in two V. vinifera varieties, Syrah (red) and Grenache (blue), in the field and during a drydown experiment in the greenhouse (Charrier et al., 2018). The piecewise linear regression was performed using the segmented function in R to estimate the values of breakpoints (Θ₁ and Θ₂) for each variety. The position of Θ₁ corresponds to the transition between anisohydric and isohydric behavior based on the change in stomatal sensitivity to VPD (for details see Charrier et al., 2018). B, Dependence of stomatal conductance (green), loss of hydraulic conductivity in petiole (purple) and stem (red), and leaf mortality (brown) on organ water potential in grapevine. Data were collected in both Syrah and Grenache, varieties but as none of the parameters defining the curves were statistically different, a mean curve was represented. Figure crafted de novo from results published in Charrier et al. (2016, 2018).