“Rising variability, not slowing down, as a leading indicator of a stochastically driven abrupt transition in a dryland ecosystem”

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Ning Chen, Ciriyam Jayaprakash, Kailiang Yu, and Vishwesha Guttal

The DOI is http://dx.doi.org/10.1086/694821

Leading indicators distinguish critical transition from stochastic transition, a long-term study in a dryland ecosystem

Rising variability, not slowing down, as a leading indicator of a stochastically driven abrupt transition in a dryland ecosystem

Ning Chen at the International Botany Congress in Shenzhen on July 25, 2017.
(Credit: Yun Zhao)

Complex real ecosystems may abruptly shift from one alternative state to another near a critical point, which is characterized by a phenomenon called critical slowing down (CSD). In this study, the authors investigate whether the leading indicators of CSD will proceed to the impending transition. Empirical tests on leading indicators on ecological systems have largely been limited to studies employing microcosms and aquatic ecosystems, but not in field systems where stochasticity can play a significant role in driving transitions. This study presents the first empirical analysis of the temporal indicators of state transition in a dryland ecosystem.

Combining empirical data and a simple modeling framework, prior to the transition the system showed no (or weak) signatures of CSD, but exhibited expected increasing trends in the variability, quantified by variance and skewness. These surprising results are consistent with the theoretical expectation of stochastically driven abrupt transitions that occur away from critical points; indeed, a driver of vegetation – annual rainfall – showed rising variance prior to the transition. The study suggests that rising variability can potentially serve as a leading indicator of stochastically driven transitions in real world ecosystems.

Overall, the changing pattern of an ecosystem between alternative states sometimes may be not determinate, but just stochastic. The authors still find some evidence to forecast it. Read the Article