Evidencing a regime shift in the North Sea using early-warning signals as indicators of critical transitions

2015 ◽  
Vol 152 ◽  
pp. 65-72 ◽  
Author(s):  
N. Wouters ◽  
V. Dakos ◽  
M. Edwards ◽  
M.P. Serafim ◽  
P.J. Valayer ◽  
...  
Keyword(s):  
North Sea ◽  
Early Warning ◽  
Regime Shift ◽  
Warning Signals ◽  
Early Warning Signals ◽  
The North ◽  
Critical Transitions ◽  
The North Sea

scholarly journals Detecting and distinguishing tipping points using spectral early warning signals

2020 ◽  
Vol 17 (170) ◽  
pp. 20200482
Author(s):  
T. M. Bury ◽  
C. T. Bauch ◽  
M. Anand
Keyword(s):  
Complex Systems ◽  
Early Warning ◽  
Dynamical Regime ◽  
Tipping Points ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Density Dependent ◽  
Critical Transitions ◽  
Fold Bifurcation ◽  
Characteristic Signal

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.

A regime shift in the North Sea circa 1988 linked to changes in the North Sea horse mackerel fishery

2001 ◽  
Vol 50 (1-2) ◽  
pp. 163-171 ◽  
Author(s):  
Philip C. Reid ◽  
Maria de Fatima Borges ◽  
Einar Svendsen
Keyword(s):  
North Sea ◽  
Regime Shift ◽  
Horse Mackerel ◽  
The North ◽  
The North Sea

scholarly journals Performance of early warning signals for disease emergence: a case study on COVID-19 data

2021 ◽  
Author(s):  
Daniele Proverbio ◽  
Françoise Kemp ◽  
Stefano Magni ◽  
Jorge Gonçalves
Keyword(s):  
Early Warning ◽  
Disease Emergence ◽  
Warning Signals ◽  
Active Monitoring ◽  
Prevalence Data ◽  
Early Warning Signals ◽  
Epidemic Dynamics ◽  
Critical Transitions ◽  
Parameter Values

AbstractThe sudden emergence of infectious diseases pose threats to societies worldwide and it is notably difficult to detect. In the past few years, several early warning signals (EWS) were introduced, to alert to impending critical transitions and extend the set of indicators for risk assessment. While they were originally thought to be generic, recent works demonstrated their sensitivity to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data is so far limited. Hence, validating their performance remains a challenge. In this study, we analyse the performance of common EWS such as increasing variance and autocorrelation in detecting the emergence of COVID-19 outbreaks in various countries, based on prevalence data. We show that EWS are successful in detecting disease emergence provided that some basic assumptions are satisfied: a slow forcing through the transitions and not fat-tailed noise. We also show cases where EWS fail, thus providing a verification analysis of their potential and limitations. Overall, this suggests that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to surveillance procedures. Our results thus represent a further step towards the application of EWS indicators for informing public health policies.

scholarly journals Early warning signals from the periphery

2021 ◽  
Author(s):  
Manfred Füllsack ◽  
Daniel Reisinger ◽  
Marie Kapeller ◽  
Georg Jäger
Keyword(s):  
Time Series ◽  
Social Interactions ◽  
Early Warning ◽  
Focus Attention ◽  
Warning Signals ◽  
Feedback Effects ◽  
Early Warning Signals ◽  
Agent Based ◽  
Critical Transitions ◽  
Micro Level

AbstractStudies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system’s components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

scholarly journals Data-driven identification of reliable sensor species to predict regime shifts in ecological networks

2020 ◽  
Vol 7 (8) ◽  
pp. 200896 ◽  
Author(s):  
Amin Ghadami ◽  
Shiyang Chen ◽  
Bogdan I. Epureanu
Keyword(s):  
Indicator Species ◽  
Early Warning ◽  
Regime Shifts ◽  
Data Driven ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Spatially Distributed ◽  
Data Driven Approach ◽  
Interacting Components

Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.

scholarly journals Dynamics of sleep: Exploring critical transitions and early warning signals

2020 ◽  
Vol 193 ◽  
pp. 105448
Author(s):  
Susanne M.M. de Mooij ◽  
Tessa F. Blanken ◽  
Raoul P.P.P. Grasman ◽  
Jennifer R. Ramautar ◽  
Eus J.W. Van Someren ◽  
...  
Keyword(s):  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions

scholarly journals Molecular analysis of Continuous Plankton Recorder samples, an examination of echinoderm larvae in the North Sea

2005 ◽  
Vol 85 (3) ◽  
pp. 451-459 ◽  
Author(s):  
Richard R Kirby ◽  
John A. Lindley
Keyword(s):  
North Sea ◽  
North Atlantic ◽  
Regime Shift ◽  
Climatic Changes ◽  
Absolute Abundance ◽  
Biological Time ◽  
The North ◽  
Visual Identification ◽  
Planktonic Larvae ◽  
The North Sea

Analysis of the biological time series of plankton samples collected by the Continuous Plankton Recorder (CPR) in the North Atlantic and North Sea has shown a regime shift in the plankton in this region. Both the distributions of planktonic organisms and their timing of occurrence in the seasonal cycle have changed and these changes appear to reflect global warming. In the North Sea the planktonic larvae of echinoderms have shown a recent dramatic increase in both relative and absolute abundance and their seasonal peak of occurrence has advanced by 47 days. The identity of the echinoderm larvae involved in this change has, however, remained equivocal. The small size of many organisms like echinoderm larvae combined with incomplete taxonomic keys hinders their visual identification and their fragility often means that useful morphological features are damaged during sampling by the CPR. Here, using new molecular methods applied to CPR samples, we show that planktonic larvae of the benthic Echinocardium cordatum dominate the North Sea plankton. We argue that since this species benefits from mild winters and warmer waters their numerical increase in the plankton is consistent with recent climatic changes that appear to be affecting the wider ecology of this region.

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