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从扰动中恢复的速度作为预测生命系统未来稳定性的一种手段。

Rate of recovery from perturbations as a means to forecast future stability of living systems.

机构信息

Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA.

Department of Internal Medicine, Medical School, University of Michigan, Ann Arbor, Michigan, 48109, USA.

出版信息

Sci Rep. 2018 Jun 18;8(1):9271. doi: 10.1038/s41598-018-27573-0.

Abstract

Anticipating critical transitions in complex ecological and living systems is an important need because it is often difficult to restore a system to its pre-transition state once the transition occurs. Recent studies demonstrate that several indicators based on changes in ecological time series can indicate that the system is approaching an impending transition. An exciting question is, however, whether we can predict more characteristics of the future system stability using measurements taken away from the transition. We address this question by introducing a model-less forecasting method to forecast catastrophic transition of an experimental ecological system. The experiment is based on the dynamics of a yeast population, which is known to exhibit a catastrophic transition as the environment deteriorates. By measuring the system's response to perturbations prior to transition, we forecast the distance to the upcoming transition, the type of the transition (i.e., catastrophic/non-catastrophic) and the future equilibrium points within a range near the transition. Experimental results suggest a strong potential for practical applicability of this approach for ecological systems which are at risk of catastrophic transitions, where there is a pressing need for information about upcoming thresholds.

摘要

预测复杂生态和生命系统中的关键转变是非常重要的,因为一旦发生转变,通常很难将系统恢复到转变前的状态。最近的研究表明,基于生态时间序列变化的几个指标可以表明系统即将发生预期的转变。然而,一个令人兴奋的问题是,我们是否可以使用远离转变的测量值来预测未来系统稳定性的更多特征。我们通过引入一种无模型的预测方法来解决这个问题,以预测实验生态系统的灾难性转变。该实验基于酵母种群的动力学,已知当环境恶化时,酵母种群会发生灾难性转变。通过在转变之前测量系统对扰动的响应,我们可以预测即将到来的转变的距离、转变的类型(即灾难性/非灾难性)以及转变附近范围内的未来平衡点。实验结果表明,对于处于灾难性转变风险中的生态系统,这种方法具有很强的实际应用潜力,因为这些系统迫切需要有关即将到来的阈值的信息。

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