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Sensitive indicators of Stipa bungeana response to precipitation under ambient and elevated CO2 concentration

机译:Stipa Bungeana对环境下沉淀的敏感指标和CO2浓度升高

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摘要

Precipitation is a primary environmental factor in the semiarid grasslands of northern China. With increased concentrations of atmospheric greenhouse gases, precipitation regimes will change, and high-impact weather events may be more common. Currently, many ecophysiological indicators are known to reflect drought conditions, but these indicators vary greatly among species, and few studies focus on the applicability of these drought indicators under high CO2 conditions. In this study, five precipitation levels (- 30%, - 15%, control, + 15%, and + 30%) were used to simulate the effects of precipitation change on 18 ecophysiological characteristics in Stipa bungeana, including leaf area, plant height, leaf nitrogen (N), and chlorophyll content, among others. Two levels of CO2 concentration (ambient, 390 ppm; 550 ppm) were used to simulate the effects of elevated CO2 on these drought indicators. Using gray relational analysis and phenotypic plasticity analysis, we found that total leaf area or leaf number (morphology), leaf water potential or leaf water content (physiology), and aboveground biomass better reflected the water status of S. bungeana under ambient and elevated CO2 than the 13 other analyzed variables. The sensitivity of drought indicators changed under the elevated CO2 condition. By quantifying the relationship between precipitation and the five most sensitive indicators, we found that the thresholds of precipitation decreased under elevated CO2 concentration. These results will be useful for objective monitoring and assessment of the occurrence and development of drought events in S. bungeana grasslands.
机译:降水是中国北方半干旱草原的主要环境因素。随着大气温室气体的浓度增加,降水制度将会发生变化,并且高抗冲天气事件可能更为常见。目前,已知许多生态学指标反映干旱条件,但这些指标在物种之间有很大差异,并且很少有研究专注于在高二氧化碳条件下对这些干旱指标的适用性。在这项研究中,使用五种( - 30%, - 15%,对照,+ 15%和+ 30%)来模拟降水变化对Stipa Bungeana的18个生理学特征的影响,包括叶面积,植物高度,叶氮(n)和叶绿素含量等。两种水平的CO 2浓度(环境,390ppm; 550ppm)用于模拟升高的CO2对这些干旱指标的影响。使用灰色关系分析和表型塑性分析,我们发现总叶面积或叶子数(形态),叶水势或叶含水含量(生理学),以及地上生物量更好地反映了环境和升高的CO2下S.Bungeana的水位比其他13个分析的变量。干旱指标在升高的CO2条件下改变了敏感性。通过量化降水与五个最敏感指标之间的关系,我们发现在升高的CO 2浓度下降水阈值降低。这些结果对于客观监测和评估S.Bungeana草原干旱事件的发生和发展有用。

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