Evaluating the performance of multivariate indicators of resilience loss
| Autor: | Rick Quax, Egbert H. van Nes, Els Weinans, Ingrid A. van de Leemput |
|---|---|
| Přispěvatelé: | Computational Science Lab (IVI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Multivariate statistics Aquatic Ecology and Water Quality Management Multidisciplinary WIMEK Computer science Mathematics and computing Science Complex system Aquatische Ecologie en Waterkwaliteitsbeheer Tipping point (climatology) 010603 evolutionary biology 01 natural sciences Article 010601 ecology Environmental sciences Order (exchange) Econometrics Life Science Medicine Set (psychology) Resilience (network) Systems biology |
| Zdroj: | Scientific Reports, 11 Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) Scientific Reports, 11:9148. Nature Publishing Group Scientific Reports Scientific Reports 11 (2021) |
| ISSN: | 2045-2322 |
| Popis: | Various complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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