1. Across plant species, drought tolerance and distributions with respect to water availability are strongly correlated with two physiological traits, the leaf water potential at wilting, that is, turgor loss point (πtlp), and the cell solute potential at full hydration, that is, osmotic potential (πo). We present methods to determine these parameters 30 times more rapidly than the standard pressure–volume (p–v) curve approach, making feasible community-scale studies of plant drought tolerance.
2. We optimized existing methods for measurements of πo using vapour-pressure osmometry of freeze-thawed leaf discs from 30 species growing in two precipitation regimes, and developed the first regression relationships to accurately estimate pressure–volume curve values of both πo and πtlp from osmometer values.
3. The πo determined with the osmometer (πosm) was an excellent predictor of the πo determined from the p–v curve (πpv,r2 = 0·80). Although the correlation of πosm and πpv enabled prediction, the relationship departed from the 1 : 1 line. The discrepancy between the methods could be quantitatively accounted for by known sources of error in osmometer measurements, that is, dilution by the apoplastic water, and solute dissolution from destroyed cell walls. An even stronger prediction of πpv could be made using πosm, leaf density (ρ), and their interaction (r2 = 0·85, all P < 2 × 10−10).
4. The πosm could also be used to predict πtlp (r2 = 0·86). Indeed, πosm was a better predictor of πtlp than leaf mass per unit area (LMA; r2 = 0·54), leaf thickness (T; r2 = 0·12), ρ (r2 = 0·63), and leaf dry matter content (LDMC; r2 = 0·60), which have been previously proposed as drought tolerance indicators. Models combining πosm with LMA, T, ρ, or LDMC or other p–v curve parameters (i.e. elasticity and apoplastic fraction) did not significantly improve prediction of πtlp.
5. This osmometer method enables accurate measurements of drought tolerance traits across a wide range of leaf types and for plants with diverse habitat preferences, with a fraction of effort of previous methods. We expect it to have wide application for predicting species responses to climate variability and for assessing ecological and evolutionary variation in drought tolerance in natural populations and agricultural cultivars.