3.4 Environmental variables affect hydraulic vulnerability segmentation
Multiple regression models with all environment variables considered, explained up to 21.7% of the variation in P 50 root – branch, and a subset of environmental variables identified in stepwise regression models explained 25.7% of the variation. Hierarchical variance partitioning suggested that aridity Index (AI), Precipitation of Wettest Quarter (Pwet), Mean Annual Precipitation (MAP) and Precipitation Seasonality (Ps) had the strongest contribution toP 50 root - branch and explained 68.3% of the total variation (Table 1). AI, with the highest individual contribution proportion (24.4%), was the major driver of the variation inP 50 root - branch, followed by Pwet (17.7%) and MAP (15.5%). Environmental factors associated with temperature including Mean annual temperature (MAT), temperature seasonality (Ts), max temperature of the warmest month (Tmax), min temperature of the coldest month (Tmin) and mean temperature of the coldest quarter (Tcold) explained 14.0% of the total variation in P 50 root – branch (Table 1). MAT had the lowest individual contribution proportion (1.3%). Several other environmental variables, including isothermality, total soil nitrogen concentration (SN), explained smaller portions (9.9%) of the total variation in P 50 root - branch than other factors (Table 1).