Responses of photosynthetic capacity to soil moisture gradient in perennial rhizome grass and perennial bunchgrass - art. no. 21

作  者:Xu ZZ, Zhou GS
影响因子:3.774
刊物名称:Bmc Plant Biology
出版年份:2011
卷:11  期:11  页码:21-21

论文摘要:

 Background: Changing water condition represents a dramatic impact on global terrestrial ecosystem productivity, mainly by limiting plant functions, including growth and photosynthesis, particularly in arid and semiarid areas. However, responses of the potential photosynthetic capacity to soil water status in a wide range of soil moisture levels, and determination of their thresholds are poorly understood. This study examined the response patterns of plant photosynthetic capacity and their thresholds to a soil moisture gradient in a perennial rhizome grass, Leymus chinensis, and a perennial bunchgrass, Stipa grandis, both dominant in the Eurasian Steppe.

Results: Severe water deficit produced negative effects on light-saturated net CO2 assimilation rate (A(sat)), stomatal conductance (g(s)), mesophyll conductance (g(m)), maximum carboxylation velocity (V-c,V-max), and maximal efficiency of PSII photochemistry (F-v/F-m). Photosynthetic activity was enhanced under moderate soil moisture with reductions under both severe water deficit and excessive water conditions, which may represent the response patterns of plant growth and photosynthetic capacity to the soil water gradient. Our results also showed that S. grandis had lower productivity and photosynthetic potentials under moderate water status, although it demonstrated generally similar relationship patterns between photosynthetic potentials and water status relative to L. chinensis.

Conclusions: The experiments tested and confirmed the hypothesis that responsive threshold points appear when plants are exposed to a broad water status range, with different responses between the two key species. It is suggested that vegetation structure and function may be shifted when a turning point of soil moisture occurs, which translates to terms of future climatic change prediction in semiarid grasslands.

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