Trait identity and functional diversity co-drive response of ecosystem productivity to nitrogen enrichment
作 者：Zhan DY, Peng YF, Li F, Yang GB, Wang J, Yu JC, Zhou GY, Yang YH*
影响因子：5.687
刊物名称：Journal of Ecology
出版年份：2019
卷：107 期：5 页码：2402-2414
论文摘要：
Exploring the mechanisms underlying the change in ecosystem productivity under anthropogenic nitrogen (N) inputs is of fundamental ecological interest. It has been proposed that functional traits, environmental factors and species richness are central drivers linking ecosystem productivity with environmental change. However, few studies have considered the joint effects of functional traits, environmental factors and species richness on ecosystem productivity under increasing N inputs.
We established a N‐manipulation experiment in a Tibetan alpine steppe in 2013. Using structural equation models, we assessed the effects of N‐induced changes in environmental factors, species richness and trait metrics (mean, variance, skewness and kurtosis of trait distribution) on gross ecosystem productivity as well as three resource use efficiencies (water, light and phosphorus (P) use efficiencies), based on measurements during the peak growing season in 2016.
We found that both light and P use efficiencies decreased under N enrichment, largely due to the N‐induced decline in functional diversity of leaf P concentration. However, both gross ecosystem productivity and water use efficiency exhibited initial increases and subsequent slight decreases with N addition. These nonlinear patterns were closely associated with both the increased morphological trait (i.e. mean of leaf area) and decreased diversity of leaf P concentration.
Synthesis. Our results illustrate how N‐induced changes in functional traits may have dual effects on ecosystem productivity: the stimulating effects of the dominant trait identity via increasing canopy light interception versus the inhibiting effect of decreasing trait diversity via declining resource use efficiencies. Our results highlight the importance of including functional traits in land surface models to improve predictions of the response of ecosystem function to N inputs.
