Appendix F: On Tipping Points and Regime Shifts

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.

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Ecological Transitions: Regime Shifts, Thresholds and Tipping Points

Oxford Bibliographies Online Datasets ◽

10.1093/obo/9780199363445-0108 ◽

2018 ◽

Author(s):

Vasilis Dakos

Keyword(s):

Regime Shifts ◽

Tipping Points

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Deep learning for early warning signals of regime shifts

10.1101/2021.03.28.437429 ◽

2021 ◽

Author(s):

Thomas Bury ◽

Raman Sujith ◽

Induja Pavithran ◽

Marten Scheffer ◽

Timothy Lenton ◽

...

Keyword(s):

Deep Learning ◽

Early Warning ◽

Normal Forms ◽

Learning Algorithm ◽

Tipping Point ◽

Regime Shifts ◽

Tipping Points ◽

Warning Signals ◽

Early Warning Signals ◽

Deep Learning Algorithm

Many natural systems exhibit regime shifts where slowly changing environmental conditions suddenly shift the system to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems all simplify down to a small number of possible 'normal forms' that determine how the new regime will look. Indicators such as increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) by detecting how dynamics slow down near the tipping point. But they do not indicate what type of new regime will emerge. Here we develop a deep learning algorithm that can detect EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behaviour of dynamics near tipping points that are common to many dynamical systems. The algorithm detects EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that will characterize the oncoming regime shift. Such approaches can help humans better manage regime shifts. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally-occurring tipping points.

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Cascading regime shifts within and across scales

10.1101/364620 ◽

2018 ◽

Cited By ~ 2

Author(s):

Juan C. Rocha ◽

Garry Peterson ◽

Örjan Bodin ◽

Simon A. Levin

Keyword(s):

Data Collection ◽

Regime Shifts ◽

Aquatic Systems ◽

Tipping Points ◽

Control Case ◽

Cascading Effects ◽

Cascading Effect ◽

Earth Systems ◽

Critical Transitions ◽

Structural Interdependence

AbstractRegime shifts are large, abrupt and persistent critical transitions in the function and structure of systems (1, 2). Yet it is largely unknown how these transitions will interact, whether the occurrence of one will increase the likelihood of another, or simply correlate at distant places. Here we explore two types of cascading effects: domino effects create one-way dependencies, while hidden feedbacks produce two-way interactions; and compare them with the control case of driver sharing which can induce correlations. Using 30 regime shifts described as networks, we show that 45% of the pair-wise combinations of regime shifts present at least one plausible structural interdependence. Driver sharing is more common in aquatic systems, while hidden feedbacks are more commonly found in terrestrial and Earth systems tipping points. The likelihood of cascading effects depends on cross-scale interactions, but differs for each cascading effect type. Regime shifts should not be studied in isolation: instead, methods and data collection should account for potential teleconnections.

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Fast environmental change and eco-evolutionary feedbacks can drive regime shifts in ecosystems before tipping points are crossed

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2021.1192 ◽

2021 ◽

Vol 288 (1955) ◽

pp. 20211192

Author(s):

P. Catalina Chaparro-Pedraza

Keyword(s):

Environmental Change ◽

Environmental Changes ◽

Phenotypic Diversity ◽

Stable State ◽

Regime Shifts ◽

Tipping Points ◽

Evolutionary Processes ◽

Alternative Stable State ◽

Management Measures ◽

The Difference

Anthropogenic environmental changes are altering ecological and evolutionary processes of ecosystems. The possibility that ecosystems can respond abruptly to gradual environmental change when critical thresholds are crossed (i.e. tipping points) and shift to an alternative stable state is a growing concern. Here I show that fast environmental change can trigger regime shifts before environmental stress exceeds a tipping point in evolving ecological systems. The difference in the time scales of coupled ecological and evolutionary processes makes ecosystems sensitive not only to the magnitude of environmental changes, but also to the rate at which changes are imposed. Fast evolutionary change mediated by high trait variation can reduce the sensitivity of ecosystems to the rate of environmental change and prevent the occurrence of rate-induced regime shifts. This suggests that management measures to prevent rate-induced regime shifts should focus on mitigating the effects of environmental change and protecting phenotypic diversity in ecosystems.

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