Post-fire Tree Mortality: Heating Increases Vulnerability to Cavitation in Longleaf Pine Branches

Tree mortality following wild and prescribed fires is of interest to both researchers and land managers. While some models exist that can predict mortality following fires, process-based models that incorporate physiological mechanisms of mortality are still being developed and improved. Delayed post-fire tree mortality has recently received increased attention, in part due to an increased use of prescribed fire as a restoration and management tool. One hypothesized mechanism of delayed mortality in trees is disruption of water transport in xylem due to exposure to the heat plume of a fire. This heat plume rapidly increases the vapor pressure deficit in the tree canopy, quickly increasing the tension on the water held in the xylem and leaves, potentially leading to cavitation. Cavitated xylem conduits can no longer transport water, eventually leading to tree death. We conducted a laboratory experiment examining whether heating stems increases their vulnerability to cavitation. We placed longleaf pine (Pinus palustris) branches in a water bath at sub-lethal temperatures (<60°C) and applied pressure in a cavitation chamber to simulate a range of xylem tension levels that may occur during fire. Percent loss of conductivity was measured following cavitation induced by various levels of applied pressure. When we compared the resulting vulnerability curves of heated branches to those of branches pressurized at room temperature, we observed increased vulnerability to cavitation in the heated samples especially at lower pressures. P50, or the pressure at which 50% of conductivity has been lost, decreased by 18% on branches heated to approximately 54°C. This suggests that stems heated during fires may be more vulnerable to cavitation, and provides some support for hydraulic disruption as a mechanism for post-fire tree mortality. Continued advancement in understanding of the mechanisms leading to delayed mortality will improve models predicting tree mortality.