Contrasting water-use patterns of Chinese fir among different plantation types in a subtropical region of China.

Abstract

Plantation cultivation plays an important role in improving terrestrial ecosystem functions and services. Understanding the water-use patterns of major afforestation species is vital for formulating ecological restoration strategies and predicting the response of plantation to climate change. However, the impacts and drivers of forest types on water-use patterns of key tree species are poorly understood. Here, the combined methods of dual stable isotope of δD and δ18O and Bayesian mixed framework (MixSIAR) were employed to investigate the water-use patterns of Cunninghamia lanceolata (Chinese fir) in a monoculture, mixed forest with Cinnamomum camphora, and mixed forest with Alnus cremastogyne under different rainfall events in subtropical China. Furthermore, the relative contribution of different soil and plant factors to the water-use patterns of Chinese fir was quantified using a random forest model. Our results showed that Chinese fir in the mixed forests (with C. camphora or with A. cremastogyne) utilized less water from shallow soil compared to that in a monoculture but significantly improved the proportion of water absorbed from deep soil with the increase of 55.57%–64.90% and 68.99%–108.83% following moderate and heavy rainfall events, respectively. The most important factors contributing to the differences in water-use patterns of Chinese fir among monoculture and mixed forests were tree attributes (i.e., leaf biomass, eco-physiological regulation, and fine root biomass). These findings reveal that Chinese fir in mixed forests could optimize water-use patterns by adjusting plant properties for interspecific niche complementarity, improving the utilization of deep soil water. Overall, this study suggests that mixed-species plantations could improve water-use efficiency and reduce the sensitivity of tree species to precipitation change, indicating they are better able to cope with expected climate variability.