Relationship between leaf nutrient resorption and economic traits and its environmental driving factors in subalpine Picea likiangensis var.balfouriana forests
Elucidating the relationship between leaf nutrient resorption and economic traits is important for understanding plant nutrient uptake and utilization strategies.However,our knowledge of whether and how leaf nutrient resorption is coordinated with leaf economic traits,particularly in ectomycorrhizal(ECM)-dominated conifers in alpine forests,remains limited.Here,the relationships between leaf nutrient resorption and economic traits and their environmental drivers were investigated by measuring leaf nitrogen(N)and phosphorous(P)resorption efficiencies,leaf morphology(leaf thickness[LT],leaf tissue density[LTD],and specific leaf area[SLA]),chemical(N and P concentrations)traits,and environmental factors of 17 ECM spruce populations(Picea likiangensis var.balfouriana)in the eastern Tibetan Plateau of China.The results showed that:(1)N resorption efficiency(NRE)and P resorption efficiency(PRE)were significantly correlated with leaf traits,among which NRE and PRE were negatively correlated with SLA and leaf N and P concentrations and positively correlated with LT and LTD.This suggests that leaf nutrient resorption is a resource-conservative trait,which functions in tandem with leaf conservative traits(LT and LTD)and trade-off with leaf acquisitive traits(SLA,leaf N and P concentrations)in leaf nutrient uptake and utilization.(2)Temperature was the dominant factor affecting NRE,PRE,and leaf economic traits.With decreasing temperature,NRE,PRE,LT,and LTD tended to increase,whereas SLA and leaf N and P concentrations tended to decrease.Collectively,this study reveals that temperature played a critical role in driving the coordination between leaf nutrient resorption and leaf economic traits.This finding enriches the understanding of the coordination of carbon and nutrient economics and its driving mechanism in alpine forests and is significant for understanding and predicting the adaptability of alpine forests in the context of climate change.