Intraspecific trait variation and coordination of leaf economics spectrum in Elaeagnus angustifolia across three basins in Central Asia

The leaf economic spectrum is a comprehensive concept to describe the coordinated changes in leaf structure and chemical and physiological traits under different resource gradients. Our overall goal is to explore the intraspecific variation of leaf traits along environmental gradients, determine the influencing factors of the leaf economic spectrum of desert plants, and reveal the adaptive strategies of desert plants. Here, we examined thirteen leaf economic traits of Elaeagnus angustifolia from three geographical populations in arid regions of China. The resource trade-off strategy of three geographical populations of E. angustifolia in the arid area was based on the area traits and quality. In addition, the population of Cele was more inclined to the survival strategy with thick leaves and weak photosynthetic capacity than those in Mosuowan and Turpan populations. The principal component analysis results showed that the first-axis and second-axis main components could explain 99.997% variation of the original thirteen traits. The results of redundancy analysis showed that precipitation had the greatest impact on the economic traits of thirteen leaves of E. angustifolia. The leaf traits of three geographical populations of E. angustifolia in arid areas have economic laws, which are mainly controlled by annual precipitation and annual evaporation. Our results will be of great significance in improving our understanding of diverse plant resource utilization strategies and predicting the structure and function of ecosystems.

Automated summary

This study explores the intraspecific variation of leaf traits in desert plants and investigates the influencing factors of the leaf economic spectrum. The results show that precipitation has the greatest impact on the leaf traits of Elaeagnus angustifolia. This research is significant for understanding plant resource utilization strategies and predicting ecosystem structure and function.