Cascading regime shifts within and across scales

Abstract

Regime 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.

Evolution and effects of the social-ecological system over a millennium in China’s Loess Plateau

“…Regime shifts, i.e. large, abrupt, and persistent changes in system structure, function, and feedbacks, occur across a wide range of SES (8,9). Identifying the evolutionary phases (regime shifts) of a SES and the drivers of regime shifts for a long time frame is critical to successful future system management (10).…”

Transient invaders can induce shifts between alternative stable states of microbial communities

“…For example, the opportunistic pathogen Clostridium difficile can take over the gut microbiome of susceptible hosts, leading to an unhealthy, highly persistent community state (14). Moreover, microbial ecosystems often undergo transitions between alternative stable states in response to environmental disturbance (15)(16)(17), analogous to regime shifts observed in macroecosystems under stress (18)(19)(20). Among the many different stresses that can alter microbial ecosystems, however, there is still very little understanding of how microbial dispersal affects the stability of communities.…”

Exploring the Space of Possibilities in Cascading Disasters with Catastrophe Dynamics

“…Applications in which catastrophic interactions are encoded in an adjacency matrix are as varied as in disease spread forecasting and risk mitigation [ 18 , 24 ], tramway infrastructure risk assessment [ 25 ], hydro-dam failure analysis [ 22 ], volcanic eruption post-crisis assessment [ 7 ], modeling of road network disruption by floods [ 26 ], or ecological disaster modeling [ 27 ]. The system’s dynamics is usually simulated, although an analytical solution to the final state can also be estimated [ 21 ].…”

“…We also narrowed the study to events which have an impact from the scale of a city to that of a continent, are relatively sudden and not excessively rare. Hence, we did not consider common or freak accidents (road, domestic, workplace events), long-term trends related to climate change [ 28 ] and ecological disasters [ 27 ], or speculative and existential risks [ 29 ]. As the focus of our work is on hazard and risk interactions, we also defined events so that there can be a clear, explicit one-to-one interaction between two events.…”