ArticleScientific reports2021
Evaluating the performance of multivariate indicators of resilience loss.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Early Warning Signals in Ecological Time-Series.Entropy (Basel, Switzerland) · 2026Review
- Spatial early warning signals to assess economic resilience.iScience · 2025Article
- Multivariate resilience indicators to anticipate vector-borne disease outbreaks: A West Nile virus case-study.PLoS computational biology · 2025Article
- Examining the research methods of early warning signals in clinical psychology through a theoretical lens.BMC psychiatry · 2025Review
- Anticipating regime shifts by mixing early warning signals from different nodes.Nature communications · 2024Article
- Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.Frontiers in network physiology · 2024Article
- Early warning signals have limited applicability to empirical lake data.Nature communications · 2023Article
- Systematic analysis and optimization of early warning signals for critical transitions using distribution data.iScience · 2023Article
- Early warnings for multi-stage transitions in dynamics on networks.Journal of the Royal Society, Interface · 2023Article
- Anticipating the occurrence and type of critical transitions.Science advances · 2023Article
- Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis.iScience · 2022Article
- Anticipating the direction of symptom progression using critical slowing down: a proof-of-concept study.BMC psychiatry · 2022Article
- Towards a Data-Driven Estimation of Resilience in Networked Dynamical Systems: Designing a Versatile Testbed.Frontiers in network physiology · 2022Article
- Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks.Frontiers in physiology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.