ArticleScience advances2023
Anticipating the occurrence and type of critical transitions.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.Nature communications · 2026Article
- Detecting and forecasting tipping points from sample variance alone.PNAS nexus · 2026Article
- Quantifying the effects of response diversity dynamics on ecosystem stability.Nature communications · 2026Article
- Navigating tipping points: A complex systems framework for anticipating lake ecosystem collapse.Fundamental research · 2026Review
- Predicting instabilities in transient landforms and interconnected ecosystems.Nature communications · 2026Article
- Catalysts and inhibitors of critical transitions in ecological systems.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Identifying fish populations prone to abrupt shifts via dynamical footprint analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Uncovering bifurcation behaviors of biochemical reaction systems from network topology.Scientific reports · 2025Article
- Quantifying the intra- and inter-species community interactions in microbiomes by dynamic covariance mapping.Nature communications · 2025Article
- Quantifying local stability and noise levels from time series in the US Western Interconnection blackout on 10th August 1996.Nature communications · 2025Article
- Deep learning for predicting the occurrence of tipping points.Royal Society open science · 2025Article
- Revealing unseen dynamical regimes of ecosystems from population time-series data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder.Journal of affective disorders · 2025Observational
- Assessing and comparing early warning signal performance in spatially-structured systems.PloS one · 2025Article
- Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.Royal Society open science · 2024Article
- Detecting shifts in nonlinear dynamics using Empirical Dynamic Modeling with Nested-Library Analysis.PLoS computational biology · 2024Article
- Article
- Early warning signals have limited applicability to empirical lake data.Nature communications · 2023Article
- Reactivity of complex communities can be more important than stability.Nature communications · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Critical transition can occur in many real-world systems. The ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWSs) have been developed to anticipate the coming critical transition or distinguish types of transition. However, no effective method allows to establish practical threshold indicating the condition when the critical transition is most likely to occur. Here, we introduce a powerful EWS, named dynamical eigenvalue (DEV), that is rooted in bifurcation theory of dynamical systems to estimate the dominant eigenvalue of the system. Theoretically, the absolute value of DEV approaches 1 when the system approaches bifurcation, while its position in the complex plane indicates the type of transition. We demonstrate the efficacy of the DEV approach in model systems with known bifurcation types and also test the DEV approach on various critical transitions in real-world systems.
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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.