Jump to content

Critical transition

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Fedichev (talk | contribs) at 17:06, 27 January 2023 (→Early-warning signals and critical slowing down). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Critical transitions are abrupt shifts in the state of ecosystems, the climate, financial systems, electric grids 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]

Early-warning signals and critical slowing down

Graphical representation of alternative stable states and the direction of critical slowing down prior to a critical transition (taken from Lever et al. 2020).[5] 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[6]
Emerging signals of declining forest resilience under climate change[6]

Significant efforts have been made to identify early-warning signals of critical transitions.[7][8][9][10][11][12][13][14] 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,[8][11][12] 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.[5] Researchers have explored early-warning signals in lakes, climate dynamics, the Amazon rainforest,[15] forests worldwide,[6] food webs, dry-land transitions and epilepsy attacks.[8]

Studies show that more than three-quarters of Amazon rainforest has been losing resilience since the early 2000s as measured by the critical slowing down (CSD)[15] and that tropical, arid and temperate forests are substantially losing resilience.[6] 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 CSD of system processes that occur at thresholds".[6]

Loss of physiological resilience, CSD, and increased temporal autocorrelation (TAC) are hallmarks of aging. In short-lived species, such as lab mice, TAC of the organism state variables persist for life and are the consequence of the dynamic instability of the organism state [16]. In longer-lived species, such as humans, the organism state is initially stable. However, TAC gradually increases, and physiological resilience is thereby reduced up to a point when the stability of the organism state is suddenly lost at some age around 60 on average and no later than 120-130 [17]. The upper bound provides the natural estimation for the maximum life span in humans (see popular discussion in [18]). The corresponding critical transition leads to reduced health and increased frailty and all-cause mortality. The analysis of longitudinal measurements from wearable sensors indicates that the fraction of patients with long TAC increases exponentially as the function of age and doubles every eight years, that is, at the rate matching the mortality rate doubling time from Gompertz mortality law [19].

According to [20], in the vicinity of a power outage (collapse), power systems experience critical slowing down (CSD), manifested as the simultaneous slowing and amplifying the system state vector fluctuations. The authors conclude that the onset of CSD is a good marker of approach to the threshold of global instability.It can be straightforwardly detected from the analysis of single-node autostructure and autocorrelation functions of system state variables and thus does not require full observability of the grid

See also

References

  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. ^ 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.
  6. ^ 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.
  7. ^ 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
  8. ^ a b c Scheffer, M., et al. (2009) Early-warning signals for critical transitions. Nature 461, 53–59
  9. ^ 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
  10. ^ Dakos, V., et al. (2010) Spatial correlation as leading indicator of catastrophic shifts. Theor Ecol 3, 163–174
  11. ^ 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
  12. ^ 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
  13. ^ van Nes, E., and Scheffer, M. (2005) Implications of spatial heterogeneity for catastrophic regime shifts in ecosystems. Ecology 86, 1797–1807
  14. ^ Hastings, A., and Wysham, D.B. (2010) Regime shifts in ecological systems can occur with no warning. Ecol Lett, 1–9
  15. ^ 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. doi:10.1038/s41558-022-01287-8. ISSN 1758-6798. S2CID 234889502.
  16. ^ Avchaciov, Konstantin; Antoch, Marina P.; Andrianova, Ekaterina L.; Tarkhov, Andrei E.; Menshikov, Leonid I.; Burmistrova, Olga; Gudkov, Andrei V.; Fedichev, Peter O. (1 November 2022). "Unsupervised learning of aging principles from longitudinal data". Nature Communications. 13 (1): 6529. doi:10.1038/s41467-022-34051-9. ISSN 2041-1723.
  17. ^ Pyrkov, Timothy V.; Avchaciov, Konstantin; Tarkhov, Andrei E.; Menshikov, Leonid I.; Gudkov, Andrei V.; Fedichev, Peter O. (25 May 2021). "Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts human lifespan limit". Nature Communications. 12 (1): 2765. doi:10.1038/s41467-021-23014-1. ISSN 2041-1723.
  18. ^ Willingham, Emily. "Humans Could Live up to 150 Years, New Research Suggests". Scientific American.
  19. ^ Pyrkov, Timothy V.; Sokolov, Ilya S.; Fedichev, Peter O. (14 March 2021). "Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience". Aging. 13 (6): 7900–7913. doi:10.18632/aging.202816. ISSN 1945-4589.
  20. ^ Podolsky, Dmitry; Turitsyn, Konstantin (16 July 2013). "Critical slowing-down as an indicator of approach to the loss of stability". doi:10.1109/SmartGridComm.2014.7007616. {{cite journal}}: Cite journal requires |journal= (help)
Retrieved from "https://en.wikipedia.org/w/index.php?title=Critical_transition&oldid=1135912824"