Hydraulic Parameters of Pressure–Volume Curves and Their Relationship with the Moisture Content of Live Fuels in Two Woody Species and an Epiphyte

Arid and semiarid ecosystems face significant water scarcity due to high evaporation rates exceeding precipitation. This study examines temporal variations in water relations of two woody species, Vachellia schaffneri (S. Watson) Seigler & Ebinger, and Prosopis laevigata (Humb. & Bonpl. ex Willd.) M.C. Johnst, and one epiphyte, Tillandsia recurvata (L.) L. (Bromeliaceae), to assess their drought tolerance and water storage capacity. We hypothesized that species with greater water storage capacity would exhibit lower drought tolerance due to reduced osmotic adjustments, whereas species with lower storage capacity would maintain turgor through osmotic regulation and cell wall rigidity. Predawn and midday water potentials (Ψpd, Ψmd) were measured, and pressure–volume (P–V) curves were used to derive parameters such as saturated water content (SWC), osmotic potential (πo), turgor loss point (ΨTLP), relative water content at ΨTLP (RWCTLP), bulk modulus of elasticity (ε), and full turgor capacitance (CFT). Significant correlations were found between CFT and ΨTLP (positive), πo (positive), and ε (negative). P. laevigata and T. recurvata exhibited higher water storage capacities (41.46 and 26.45 MPa−1, respectively) but had a lower ability to maintain cell turgor under drought conditions. In contrast, V. schaffneri exhibited the lowest water storage capacity (11.88 MPa−1) but demonstrated the highest ability to maintain cell turgor (ΨTLP = −1.31 MPa) and superior osmotic adjustments (πo = −0.59 MPa). Both V. schaffneri and P. laevigata exhibited rigid cell walls, whereas T. recurvata displayed greater elasticity in its cell structures. The lowest moisture content in V. schaffneri suggests increased flammability and fire spread potential. Future studies should focus on live fuel moisture content across more species, explore seasonal variations in hydraulic traits, and integrate these physiological parameters into fire risk models to enhance wildfire prediction and management.