Warming stabilizes alpine ecosystem facing extreme rainfall events by changing plant species composition

Abstract

Climate warming and extreme climatic events are threatening ecosystem processes and functions. However, it remains unclear how climate warming changes ecosystem stability in facing extreme rainfall events. Here, we investigated the temporal stability of above‐ground net primary productivity (ANPP) in an alpine meadow via a 5‐year warming experiment, during which two flooding events occurred, in an alpine meadow. We first found that warming significantly increased the temporal stability of ANPP in facing flooding events by increasing resistance to and decreasing recovery from flooding events. Second, we found that warming shifted the plant community structure by increasing the dominance of grasses and reducing species richness and asynchrony. Last, we detected the higher temporal stability of ANPP under warming, which was mainly ascribed to the warming‐induced increase in dominant species stability (Deschampsia caespitosa). Synthesis. These findings indicate that climate warming may mitigate the shock of flooding events on the temporal stability of community productivity via altering plant community structure in alpine grasslands. Our study highlights that to buffer ecosystems from climatic extremes, we should focus on promoting the maintenance or selection of dominant species rather than only focusing on increasing species richness.

Nitrogen deposition differentially regulates the sensitivity of gross primary productivity to extreme drought versus wetness

“…For the proportions of four functional types, we only included sedges in the model, as the correlation coefficients of the extreme dry sensitivity of GPP with other three functional types were either insignificant or became insignificant after controlling for the role of sedges (Figure 5; Table S2). Then, we constructed a priori models including all plausible pathways and obtained the final SEM by eliminating insignificant pathways to achieve the lowest Akaike information criterion (AIC) (Figure S1; Chen, Fang, et al., 2021; Grace, 2006; Yan et al., 2023; Zhang, Tian, et al., 2022). Finally, linear regression was used to test the relationship between ambient N deposition and the sensitivity of GPP to extreme precipitation at the global scale.…”