Extreme Drought Decreases the Stability of Above- but Not Below-Ground Productivity Across Eurasian Steppes

文献类型： 外文期刊

第一作者： Yan, Yingjie

作者： Yan, Yingjie;Yu, Qiang;Xu, Chong;Wang, Hongqiang;Ke, Yuguang;Wu, Honghui;Hautier, Yann;Wang, Jinsong;Cheng, Changjin;Zuo, Xiaoan;Luo, Wentao;Smith, Melinda D.;Knapp, Alan K.;Smith, Melinda D.;Knapp, Alan K.;Collins, Scott L.

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关键词： climatic extreme; coordinated experiment; ecosystem stability; grassland; plant community; productivity

期刊名称：GLOBAL CHANGE BIOLOGY （ 影响因子：12.0； 五年影响因子：14.0 ）

ISSN： 1354-1013

年卷期： 2025 年 31 卷 6 期

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收录情况： SCI

摘要： Ecological stability plays a crucial role in determining the sustainability of ecosystem functioning and nature's contribution to people. Although the disruptive effects of extreme drought on ecosystem structure and functions are widely recognized, their effect on the stability of above- and belowground productivity remains understudied. We assessed the effects of drought on ecosystem stability using a 3-year drought experiment established in six Eurasian steppe grasslands. The treatments imposed included ambient precipitation, chronic drought (66% reduction in precipitation throughout the growing season), and intense drought (complete exclusion of precipitation for two months during the growing season). We found that drought, irrespective of how it was imposed, reduced the stability of aboveground net primary productivity (ANPP) but had little impact on belowground net primary productivity (BNPP) stability. Reduced ANPP stability under drought was primarily attributed to changes in subordinate species stability, with mean annual precipitation (MAP) and its variability, historical drought frequency, and the aridity index (AI) also influencing responses to extreme drought. In contrast, BNPP stability was not related to any community factor investigated, but it was influenced by MAP variability and AI. Our findings that above- and belowground productivity stability in grasslands are differentially sensitive to multi-year extreme drought under both common (MAP and AI) as well as unique drivers (plant community changes) highlight the complexity of predicting carbon cycle dynamics as hydrological extremes become more severe.

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