School of Biosciences, The University of Melbourne, Parkville, Vic., 3010, Australia.
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, 8057, Switzerland.
Ecol Lett. 2018 Jun;21(6):905-919. doi: 10.1111/ele.12948. Epub 2018 Mar 30.
In the face of global biodiversity declines, predicting the fate of biological systems is a key goal in ecology. One popular approach is the search for early warning signals (EWSs) based on alternative stable states theory. In this review, we cover the theory behind nonlinearity in dynamic systems and techniques to detect the loss of resilience that can indicate state transitions. We describe the research done on generic abundance-based signals of instability that are derived from the phenomenon of critical slowing down, which represent the genesis of EWSs research. We highlight some of the issues facing the detection of such signals in biological systems - which are inherently complex and show low signal-to-noise ratios. We then document research on alternative signals of instability, including measuring shifts in spatial autocorrelation and trait dynamics, and discuss potential future directions for EWSs research based on detailed demographic and phenotypic data. We set EWSs research in the greater field of predictive ecology and weigh up the costs and benefits of simplicity vs. complexity in predictive models, and how the available data should steer the development of future methods. Finally, we identify some key unanswered questions that, if solved, could improve the applicability of these methods.
面对全球生物多样性的减少，预测生物系统的命运是生态学的一个关键目标。一种流行的方法是根据替代稳定状态理论寻找早期预警信号（EWS）。在这篇综述中，我们介绍了动力系统中非线性背后的理论和检测可能表明状态转变的恢复力丧失的技术。我们描述了从临界减速现象中得出的通用基于丰度的不稳定性信号的研究，这代表了 EWS 研究的起源。我们强调了在生物系统中检测这种信号所面临的一些问题 - 这些系统本质上是复杂的，并且表现出低信噪比。然后，我们记录了关于不稳定替代信号的研究，包括测量空间自相关和性状动态的变化，并讨论了基于详细的人口统计和表型数据的 EWS 研究的潜在未来方向。我们将 EWS 研究置于预测生态学的更大领域中，并权衡预测模型中简单性与复杂性的成本和收益，以及可用数据应如何指导未来方法的发展。最后，我们确定了一些关键的未解决问题，如果这些问题得到解决，可能会提高这些方法的适用性。
