Divergent patterns and drivers of leaf functional traits of Robinia pseudoacacia and Pinus tabulaeformis plantations along a precipitation gradient in the Loess plateau, China.

Changes in precipitation patterns in arid and semi-arid regions can reshape plant functional traits and significantly affect ecosystem functions. However, the synchronous responses of leaf economical, anatomical, photosynthetic, and biochemical traits to precipitation changes and their driving factors have rarely been investigated, which hinders our understanding of plants' ecological adaptation strategies to drought tolerance in arid areas. Therefore, the leaf traits of two typical plantations (Robinia pseudoacacia, RP and Pinus tabulaeformis, PT) along the precipitation gradient in the Loess Plateau, including economical, anatomical, photosynthetic, and biochemical traits, were investigated in this study. The results show that the leaf photosynthetic traits of RP and PT increase along the precipitation gradient, whereas leaf biochemical traits decrease. The anatomical traits of PT decrease with increasing precipitation, whereas no significant variation was observed for RP. Random Forest analysis show that LNC, LDMC, Chl, and PRO are leaf traits that significantly vary with the precipitation gradient in both plantations. Correlation analysis reveals that the traits coordination of RP is better than that of PT. The LMG model was used to determine driving factors. The results suggest that MAP explains the variation of PT leaf traits better (30.38%-36.78%), whereas SCH and SPH contribute more to the variation of RP leaf traits (20.88%-41.76%). In addition, the piecewise Structural Equation Model shows that the climate and soil physical and chemical properties directly affect the selected leaf functional traits of RP, whereas only the soil chemical properties directly affect the selected leaf functional traits of PT. The results of this study contribute to the understanding of the ecological adaptation of plants to environmental gradients and highlight that correlations among leaf traits should be considered when predicting plant adaptation strategies under future global change scenarios.