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Anticipating Critical Transitions
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All Change: Research on early warning signals for critical transitions in complex systems such as ecosystems, climate, and global finance systems recently has been gathering pace. At the same time, studies on complex networks are starting to reveal which architecture may cause systems to be vulnerable to systemic collapse. Scheffer et al. (p. 344 ) review how previously isolated lines of work can be connected, conclude that many critical transitions (such as escape from the poverty trap) can have positive outcomes, and highlight how the new approaches to sensing fragility can help to detect both risks and opportunities for desired change.
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“…exposure to past shocks, cross-country income inequalities, export fluctuations (Distefano et al. 2018), heterogeneity among trade partners (Scheffer et al. 2012), national indicators on maintenance of diversity and connectivity in trade (Kummu et al. 2020), and population trends…”
Changes in spatial variance during a grassland to shrubland state transition
Ratajczak, Z., D’Odorico, P., Nippert, J. B., Collins, S. L., Brunsell, N. A., & Ravi, S. (2016) · Journal of Ecology · doi:10.1111/1365-2745.12696 · Cited by 58
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Abrupt climate change as a rate-dependent cascading tipping point
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“Based on this, we propose an early warning indicator to detect rate-induced tipping; so far only early warning signals specific to bifurcation tipping are known (Held and Kleinen, 2004; Dakos et al., 2008; Scheffer et al., 2009, 2012).”
BackgroundSuzuki, K. T., Nakaoka, S., Fukuda, S., & Masuya, H. (2021) · Ecological Monographs · doi:10.1002/ecm.1469 · Cited by 57
Meng, Q., Liu, S., Guo, Y., Hu, Y., Yu, Z., Bello, A., Wang, Z., Xu, W., & Xu, X. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-23599-0 · Cited by 57
Early warning signals have limited applicability to empirical lake data
O’Brien, D. A., Deb, S., Gal, G., Thackeray, S. J., Dutta, P. S., Matsuzaki, S. S., May, L., & Clements, C. F. (2023) · Nature Communications · doi:10.1038/s41467-023-43744-8 · Cited by 57
Pattern destabilization and emotional processing in cognitive therapy for personality disorders
Hayes, A. M., & Yasinski, C. W. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00107 · Cited by 64
Long-Term Dynamics and Hotspots of Change in a Desert Grassland Plant Community
Collins, S. L., & Xia, Y. (2014) · The American Naturalist · doi:10.1086/679315 · Cited by 57
Early Warning Signs in Social-Ecological Networks
Suweis, S. S., & D’Odorico, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101851 · Cited by 58
Guttal, V., Raghavendra, S., Goel, N., & Hoarau, Q. (2016) · PLoS ONE · doi:10.1371/journal.pone.0144198 · Cited by 60
Ecological shift and resilience in China's lake systems during the last two centuries
Zhang, K., Dong, X., Yang, X., Kattel, G., Zhao, Y., & Wang, R. (2018) · Global and Planetary Change · doi:10.1016/j.gloplacha.2018.03.013 · Cited by 57
Data-Driven Gearbox Failure Detection in Industrial Robots
Vallachira, S., Orkisz, M., Norrlöf, M., & Butail, S. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2912809 · Cited by 57
“3) Including Local Temporal Variation: It has been shown that change in variance in time-series can be a reliable predictor of a sudden event [11], therefore, we include local variation to improve detection performance.”
BackgroundMethod
Metadata
- Authors
- Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., van de Koppel, J., van de Leemput, I. A., Levin, S. A., van Nes, E. H., Pascual, M., & Vandermeer, J.
- Affiliations
- Wageningen University & Research (NL)
- University of Wisconsin–Madison (US)
- University of Exeter (GB)
- Consejo Superior de Investigaciones Científicas (ES)
- Spatial Ecology Department, Royal Netherlands Institute for Sea Research (NIOZ), Post Office Box 140, 4400AC, Yerseke, Netherlands.
- Department of Environmental Sciences, Wageningen University, Post Office Box 47, NL-6700 AA Wageningen, Netherlands.
- Princeton University (US)
- Santa Fe Institute (US)
- Howard Hughes Medical Institute (US)
- ORCID
- Venue
- Science
- Published
- 2012-10-19
- Volume
- 338
- Issue
- 6105
- Pages
- 344-348
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2153268540
- Cited by
- 2,208
- References
- 71
- Citations by year
- 2026: 170 · 2025: 174 · 2024: 149 · 2023: 175 · 2022: 197 · 2021: 200 · 2020: 185 · 2019: 196 · 2018: 151 · 2017: 154 · 2016: 135 · 2015: 120 · 2014: 118 · 2013: 80 · 2012: 4
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- Flanders Marine Institute (Flanders Marine Institute) (submittedVersion)
- HAL (submittedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- NIOZ Publications (Royal Netherlands Institute for Sea Research) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Science Foundation (1144683)
- Howard Hughes Medical Institute
- Natural Environment Research Council (NE/F005474/1)
- Division of Environmental Biology (1144683)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Ecosystem dynamics and resilience
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- tipping points, complex systems, early warning indicators, ecological networks, financial markets, system collapse
- MeSH terms
- Animals, Forecasting, Humans, Risk Assessment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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