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Critical transition

Summary

Critical transitions are abrupt shifts in the state of ecosystems, the climate, financial systems or other complex dynamical systems that may occur when changing conditions pass a critical or bifurcation point. As such, they are a particular type of regime shift. Recovery from such shifts may require more than a simple return to the conditions at which a transition occurred, a phenomenon called hysteresis.[1][2][3][4] In addition to natural systems, critical transitions are also studied in psychology,[5] medicine,[6][7] economics,[8][9] sociology,[10] military,[11] and several other disciplines.

Early-warning signals edit

Critical slow down edit

 
Graphical representation of alternative stable states and the direction of critical slowing down prior to a critical transition (taken from Lever et al. 2020).[12] Top panels (a) indicate stability landscapes at different conditions. Middle panels (b) indicate the rates of change akin to the slope of the stability landscapes, and bottom panels (c) indicate a recovery from a perturbation towards the system's future state (c.I) and in another direction (c.II).
 
Temporal variations of forest resilience and its key drivers[13]
 
Emerging signals of declining forest resilience under climate change[13]

Significant efforts have been made to identify early-warning signals of critical transitions.[14][15][16][17][18][19][20][21] Systems approaching a bifurcation point show a characteristic behaviour called critical slowing down leading to an increasingly slow recovery from perturbations. This, in turn, may lead to an increase in (spatial or temporal) autocorrelation and variance, while variance spectra tend to lower frequencies,[15][18][19] and the 'direction of critical slowing down' in a system's state space may be indicative of a system's future state when delayed negative feedbacks leading to oscillatory or other complex dynamics are weak.[12] Researchers have explored early-warning signals in lakes, climate dynamics, the Amazon rainforest,[22] forests worldwide,[13] food webs, dry-land transitions and epilepsy attacks.[15]

Examples edit

Studies show that more than three-quarters of Amazon rainforest has been losing resilience since the early 2000s as measured by CSD[22] and that tropical, arid and temperate forests are substantially losing resilience.[13] It has been proposed that a loss of resilience in forests "can be detected from the increased temporal autocorrelation (TAC) in the state of the system, reflecting a decline in recovery rates due to the critical slowing down (CSD) of system processes that occur at thresholds".[13]

Flickering edit

The above approach (looking for critical slow down) is how most researchers assess if a critical transition is imminent. However, in highly stochastic (random) systems, alternative basins of attraction will be reached well before bifurcation points are reached.[23] Perturbations might therefore cause the system to 'flicker' between the basins of attraction.

Examples edit

This idea has gained considerable interest in the last few years, somewhat entering the mainstream.[24] The idea has been applied widely, to studies of ecological resilience[25] (such as eutrophication of a lake [26]) and to larger systems such as the potential collapse of the Atlantic Meridional Overturning Circulation.[27]

See also edit

References edit

  1. ^ Scheffer, Marten; Carpenter, Steve; Foley, Jonathan A.; Folke, Carl; Walker, Brian (October 2001). "Catastrophic shifts in ecosystems". Nature. 413 (6856): 591–596. Bibcode:2001Natur.413..591S. doi:10.1038/35098000. ISSN 1476-4687. PMID 11595939. S2CID 8001853.
  2. ^ Scheffer, Marten (26 July 2009). Critical transitions in nature and society. Princeton University Press. ISBN 978-0691122045.
  3. ^ Scheffer, Marten; Bascompte, Jordi; Brock, William A.; Brovkin, Victor; Carpenter, Stephen R.; Dakos, Vasilis; Held, Hermann; van Nes, Egbert H.; Rietkerk, Max; Sugihara, George (September 2009). "Early-warning signals for critical transitions". Nature. 461 (7260): 53–59. Bibcode:2009Natur.461...53S. doi:10.1038/nature08227. ISSN 1476-4687. PMID 19727193. S2CID 4001553.
  4. ^ Scheffer, Marten; Carpenter, Stephen R.; Lenton, Timothy M.; Bascompte, Jordi; Brock, William; Dakos, Vasilis; Koppel, Johan van de; Leemput, Ingrid A. van de; Levin, Simon A.; Nes, Egbert H. van; Pascual, Mercedes; Vandermeer, John (19 October 2012). "Anticipating Critical Transitions". Science. 338 (6105): 344–348. Bibcode:2012Sci...338..344S. doi:10.1126/science.1225244. hdl:11370/92048055-b183-4f26-9aea-e98caa7473ce. ISSN 0036-8075. PMID 23087241. S2CID 4005516.
  5. ^ van de Leemput, Ingrid A.; Wichers, Marieke; Cramer, Angélique O. J.; Borsboom, Denny; Tuerlinckx, Francis; Kuppens, Peter; van Nes, Egbert H.; Viechtbauer, Wolfgang; Giltay, Erik J.; Aggen, Steven H.; Derom, Catherine; Jacobs, Nele; Kendler, Kenneth S.; van der Maas, Han L. J.; Neale, Michael C. (2014-01-07). "Critical slowing down as early warning for the onset and termination of depression". Proceedings of the National Academy of Sciences. 111 (1): 87–92. doi:10.1073/pnas.1312114110. ISSN 0027-8424. PMC 3890822. PMID 24324144.
  6. ^ Trefois, Christophe; Antony, Paul MA; Goncalves, Jorge; Skupin, Alexander; Balling, Rudi (2015-08-01). "Critical transitions in chronic disease: transferring concepts from ecology to systems medicine". Current Opinion in Biotechnology. Systems biology • Nanobiotechnology. 34: 48–55. doi:10.1016/j.copbio.2014.11.020. ISSN 0958-1669.
  7. ^ de Mooij, Susanne M. M.; Blanken, Tessa F.; Grasman, Raoul P. P. P.; Ramautar, Jennifer R.; Van Someren, Eus J. W.; van der Maas, Han L. J. (2020-09-01). "Dynamics of sleep: Exploring critical transitions and early warning signals". Computer Methods and Programs in Biomedicine. 193: 105448. doi:10.1016/j.cmpb.2020.105448. ISSN 0169-2607.
  8. ^ Smug, D. (2018) Critical Transitions in financial models: Bifurcation- and noise-induced phenomena https://ore.exeter.ac.uk/repository/handle/10871/36063?show=full
  9. ^ Xing, Kai; Yang, Xiaoguang (2020-01-01). "Predicting default rates by capturing critical transitions in the macroeconomic system". Finance Research Letters. 32: 101107. doi:10.1016/j.frl.2019.02.007. ISSN 1544-6123.
  10. ^ Mascareño, Aldo (2020-12-01). "Close to the Edge: From Crisis To Critical Transitions in Social Systems Theory". Soziale Systeme. 25 (2): 251–276. doi:10.1515/sosys-2020-0026. ISSN 2366-0473.
  11. ^ Šlebir, Miha (2022-06-15). "TOWARDS UNDERSTANDING CRITICAL TRANSITIONS IN WARFARE". Obrana a strategie (Defence and Strategy). 22 (1): 055–074. doi:10.3849/1802-7199.22.2022.01.055-074.
  12. ^ a b Lever, J. Jelle; Leemput, Ingrid A.; Weinans, Els; Quax, Rick; Dakos, Vasilis; Nes, Egbert H.; Bascompte, Jordi; Scheffer, Marten (2020). "Foreseeing the future of mutualistic communities beyond collapse". Ecology Letters. 23 (1): 2–15. doi:10.1111/ele.13401. PMC 6916369. PMID 31707763.
  13. ^ a b c d e Forzieri, Giovanni; Dakos, Vasilis; McDowell, Nate G.; Ramdane, Alkama; Cescatti, Alessandro (August 2022). "Emerging signals of declining forest resilience under climate change". Nature. 608 (7923): 534–539. doi:10.1038/s41586-022-04959-9. ISSN 1476-4687. PMC 9385496. PMID 35831499.
    • News article: "Forests are becoming less resilient because of climate change". New Scientist. Retrieved 21 August 2022.
  14. ^ Biggs, R., et al. (2009) Turning back from the brink: Detecting an impending regime shift in time to avert it. P Natl Acad Sci Usa 106, 826–831
  15. ^ a b c Scheffer, M., et al. (2009) Early-warning signals for critical transitions. Nature 461, 53–59
  16. ^ Contamin, R., and Ellison, A.M. (2009) Indicators of regime shifts in ecological systems: What do we need to know and when do we need to know it? Ecol. Appl. 19, 799–816
  17. ^ Dakos, V., et al. (2010) Spatial correlation as leading indicator of catastrophic shifts. Theor Ecol 3, 163–174
  18. ^ a b Dakos, V., et al. (2008) Slowing down as an early warning signal for abrupt climate change. P Natl Acad Sci Usa 105, 14308–14312
  19. ^ a b van Nes, E.H., and Scheffer, M. (2007) Slow recovery from perturbations as a generic indicator of a nearby catastrophic shift. Am. Nat. 169, 738–747
  20. ^ van Nes, E., and Scheffer, M. (2005) Implications of spatial heterogeneity for catastrophic regime shifts in ecosystems. Ecology 86, 1797–1807
  21. ^ Hastings, A., and Wysham, D.B. (2010) Regime shifts in ecological systems can occur with no warning. Ecol Lett, 1–9
  22. ^ a b Boulton, Chris A.; Lenton, Timothy M.; Boers, Niklas (March 2022). "Pronounced loss of Amazon rainforest resilience since the early 2000s". Nature Climate Change. 12 (3): 271–278. Bibcode:2022NatCC..12..271B. doi:10.1038/s41558-022-01287-8. ISSN 1758-6798. S2CID 234889502.
    • News article about the study: "Climate crisis: Amazon rainforest tipping point is looming, data shows". The Guardian. 7 March 2022. Retrieved 18 April 2022.
  23. ^ Dakos, Vasilis (2013). "Flickering as an early warning signal". Theoretical Ecology. 6.
  24. ^ Monbiot, George. "The 'flickering' of Earth systems is warning us: act now, or see our already degraded paradise lost". The Guardian. Retrieved 16 December 2023.
  25. ^ Scheffer, Marten (2015). "Generic indicators of ecological resilience: inferring the chance of a critical transition". {{cite journal}}: Cite journal requires |journal= (help)
  26. ^ Rong, Wang (2012). "Flickering gives early warning signals of a critical transition to a eutrophic lake state". {{cite journal}}: Cite journal requires |journal= (help)
  27. ^ Boers, Niklas (August 2021). "Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation". Nature Climate Change. 11 (8): 680–688. Bibcode:2021NatCC..11..680B. doi:10.1038/s41558-021-01097-4. S2CID 236930519. Retrieved 5 August 2021.

External links edit

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