Flickering gives early warning signals of a critical transition to a eutrophic lake state
Flickering gives early warning signals of a critical transition to a eutrophic lake state
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DOI：
10.1038/nature11655
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发表时间：
2012-12-20
期刊：
影响因子：
64.8
通讯作者：
Scheffer, Marten
中科院分区：
文献类型：
--
作者：
Wang, Rong;Dearing, John A.;Scheffer, Marten
人们认识到需要预测社会生态系统中的临界点或关键转变（1,2）。对数学系统（3-5）和实验系统（6-9）的研究表明，系统可能在临界过渡之前“摇摆”。这种预警信号（10）可能是由于临界减速现象，导致系统从较小的冲击中缓慢恢复，也可能是由于闪烁现象，导致系统在响应较大的冲击时在可选状态之间来回切换。这种社会-生态系统转变的信号很少被观察到（11），尤其是因为通常需要高分辨率的时间序列。本文将湖泊集水区系统的经验数据与数学模型相结合，证明了从稀疏数据中可以检测到闪烁。在经验记录和模型输出中，我们展示了在向富营养化湖泊条件过渡的10-30年前，方差的上升与自相关和偏度的下降是如何开始的，这一发现与闪烁一致，而不是临界减速（4,12）。我们的研究结果表明，如果环境制度受到巨大的外部影响的充分影响，导致闪烁，那么现代社会生态系统过渡的早期预警信号可能比以前认为的更强，因此更容易识别。
There is a recognized need to anticipate tipping points, or critical transitions, in social-ecological systems(1,2). Studies of mathematical(3-5) and experimental(6-9) systems have shown that systems may 'wobble' before a critical transition. Such early warning signals(10) may be due to the phenomenon of critical slowing down, which causes a system to recover slowly from small impacts, or to a flickering phenomenon, which causes a system to switch back and forth between alternative states in response to relatively large impacts. Such signals for transitions in social-ecological systems have rarely been observed(11), not the least because high-resolution time series are normally required. Here we combine empirical data from a lake-catchment system with a mathematical model and show that flickering can be detected from sparse data. We show how rising variance coupled to decreasing autocorrelation and skewness started 10-30 years before the transition to eutrophic lake conditions in both the empirical records and the model output, a finding that is consistent with flickering rather than critical slowing down(4,12). Our results suggest that if environmental regimes are sufficiently affected by large external impacts that flickering is induced, then early warning signals of transitions in modern social-ecological systems may be stronger, and hence easier to identify, than previously thought.