生态系统
陆地生态系统
环境科学
时间尺度
碳通量
生态学
环境资源管理
气候变化
碳循环
适应(眼睛)
生物
神经科学
作者
Luping Qu,Jiquan Chen,Jingfeng Xiao,Hans J. De Boeck,Gang Dong,Shicheng Jiang,Ya-Lin Hu,Yixuan Wang,Changliang Shao
标识
DOI:10.1016/j.envres.2023.117495
摘要
Extreme heatwaves have become more frequent and severe in recent decades, and are expected to significantly influence carbon fluxes at regional scales across global terrestrial ecosystems. Nevertheless, accurate prediction of future heatwave impacts remains challenging due to a lack of a consistent comprehension of intrinsic and extrinsic mechanisms. We approached this knowledge gap by analyzing the complexity factors in heatwave studies, including the methodology for determining heatwave events, divergent responses of individual ecosystem components at multiple ecological and temporal scales, and vegetation status and hydrothermal environment, among other factors. We found that heatwaves essentially are continuously changing compound environmental stress that can unfold into multiple chronological stages, and plant physiology and carbon flux responses differs in each of these stages. This approach offers a holistic perspective, recognizing that the impacts of heatwaves on ecosystems can be better understood when evaluated over time. These stages include instantaneous, post-heatwave, legacy, and cumulative effects, each contributing uniquely to the overall impact on the ecosystem carbon cycle. Next, we investigated the importance of the timing of heatwaves and the possible divergent consequences caused by different annual heatwave patterns. Finally, a conceptual framework is proposed to establish a united foundation for the study and comprehension of the consequences of heatwaves on ecosystem carbon cycle. This instrumental framework will assist in guiding regional assessments of heatwave impacts, shedding light on the underlying mechanisms responsible for the varied responses of terrestrial ecosystems to specific heatwave events, which are imperative for devising efficient adaptation and mitigation approaches.
科研通智能强力驱动
Strongly Powered by AbleSci AI