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Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

机译:稳定性和弹性之间的关系决定了不同环境驱动因素下预警信号的性能

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摘要

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.
机译:人们已经发现时间波动的变化模式表明了从生态群落到人类生理学的各种系统中的关键转变。但是,在某些系统中,这些预警信号的故障要求更好地了解其局限性。特别是,在不同的恶化环境中对预警信号的普遍性知之甚少。在这项研究中,我们表征了多种环境驱动因素如何影响实验室酵母菌种群的动态,先前已显示出其可替代的稳定状态[Dai et al。,Science,2012]。我们观察到,在两个缓慢恶化的环境中,变异系数和自相关系数均在种群崩溃之前增加,一个环境死亡率上升,另一个环境养分利用率下降。我们将跨多个环境的预警信号的性能作为“弹性丧失的指标”进行了比较。我们发现,变化的性能取决于系统对特定驱动程序变化的响应方式,可以通过稳定性(恢复率)和弹性(吸引池的大小)之间的关系来捕获。此外,我们证明了稳定性和弹性之间的正相关性(作为基于临界减速的指标的基本假设)在同时更改多个环境驱动因素的情况下可能会破坏该系统。我们的结果表明,在不同情况下应用预警信号需要更好地理解稳定性-弹性关系。

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