On the use of the minimum leaf water potential as a measure of exposure to hydraulic risk in plants

Background/Question/Methods

The minimum water potential (Ψ_min) describes the highest xylem tension that a species (or population or individual plant) can tolerate. This metric is increasingly used to characterize exposure to hydraulic risk and to calculate hydraulic safety margins (HSM), defined as the difference between Ψ_min and a critical water potential causing hydraulic dysfunction (e.g., Ψ₅₀, or the water potential causing 50% loss of hydraulic conductivity). HSM has the merit of integrating in a single metric with consistent units a measure of absolute stress tolerance (Ψ₅₀) with a measure of (extreme) exposure at the tissue level (Ψ_min), thus yielding a promising measure of mortality risk. The determination of Ymin, however, is challenging. The most common approach is to estimate Ψmin as the minimum leaf water potential measured in a given species or population. However, this is problematic, as sample extremes are inherently biased and the magnitude of the bias depends on the probability distribution of the underlying variable and on sample size, which is frequently low and unevenly distributed across sampling units. Clearly, a more robust way of determining Ψmin (and other ecophysiological extremes) is needed. Here, we compare different approaches to estimate Ψmin using a combination of empirical and synthetic datasets.

Results/Conclusions

We first show that the distribution of raw water potential measurements differs across species, and this variability can be associated to different strategies of water potential regulation (iso- vs anisohydry). Secondly, we show that traditional approaches to estimate Ψmin, based on sample extremes, can lead to highly biased and inconsistent estimates. We then use extreme values theory to provide a general framework aimed at providing more robust estimates of Ψmin, which can be also applied to other ecophysiological extremes. Finally, we assess the implications of our results for the characterization of hydraulic risk and its variability across species, and propose a statistically robust measure of the hydraulic safety margin.