Early-Warning Signals For Cenozoic Climate Transitions

2021 ◽  
Author(s):  
Georg Klinghammer ◽  
Christopher Böttner
Keyword(s):  
Standard Deviation ◽  
Early Warning ◽  
Large Scale ◽  
Basin Of Attraction ◽  
Warning Signals ◽  
Climate History ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Carbon And Oxygen Isotope ◽  
Climate Transitions

<p>Paleoclimatic records document large-scale shifts in the Earth’s climate history. Among other possibilities, these transitions might have been caused by bifurcations in the leading dynamical modes. Such bifurcation-induced critical transitions are typically preceded by characteristic early-warning signals (EWS), for example in terms of rising standard deviation and lag-one autocorrelation. These EWS are caused by the phenomenon of critical slowing down (CSD) in response to a widening of the underlying basin of attraction as the bifurcation is approached. The presence of EWS prior to an observed transition therefore provides evidence that the transition is caused by a bifurcation. We reveal significant EWS prior to several critical transitions within a paleoclimate record spanning the Cenozoic Era, i.e., the last 67M years. We employed the CENOzoic Global Reference benthic foraminifer carbon and oxygen Isotope Dataset (CENOGRID), comprising two time series of isotope variations of δ<sup>18</sup>O and δ<sup>13</sup>C. The standard deviation and lag-one autocorrelation are estimated in sliding windows for both records, to reveal whether CSD occurs ahead of the major abrupt transitions in these records. Specifically, we detect significant EWS for five out of nine previously identified transitions in at least one of the two available records. EWS are recognized for significant increases in both CSD indicators prior to the transition. Our results hence suggest that at least five major climate transitions of the last 67 Ma were triggered by bifurcations in leading modes of variability, indicating bifurcations have likely played a key role in the deep-time evolution of the Earth's climate system.</p>

scholarly journals Anticipating critical transitions in epithelial–hybrid-mesenchymal cell-fate determination

2019 ◽  
Vol 116 (52) ◽  
pp. 26343-26352 ◽  
Author(s):  
Sukanta Sarkar ◽  
Sudipta Kumar Sinha ◽  
Herbert Levine ◽  
Mohit Kumar Jolly ◽  
Partha Sharathi Dutta
Keyword(s):  
Cell Fate ◽  
Early Warning ◽  
Messenger Rna ◽  
Cancer Metastasis ◽  
Basin Of Attraction ◽  
Disease Onset ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Stochastic Perturbations ◽  
Critical Transitions

In the vicinity of a tipping point, critical transitions occur when small changes in an input condition cause sudden, large, and often irreversible changes in the state of a system. Many natural systems ranging from ecosystems to molecular biosystems are known to exhibit critical transitions in their response to stochastic perturbations. In diseases, an early prediction of upcoming critical transitions from a healthy to a disease state by using early-warning signals is of prime interest due to potential application in forecasting disease onset. Here, we analyze cell-fate transitions between different phenotypes (epithelial, hybrid-epithelial/mesenchymal [E/M], and mesenchymal states) that are implicated in cancer metastasis and chemoresistance. These transitions are mediated by a mutually inhibitory feedback loop—microRNA-200/ZEB—driven by the levels of transcription factor SNAIL. We find that the proximity to tipping points enabling these transitions among different phenotypes can be captured by critical slowing down-based early-warning signals, calculated from the trajectory of ZEB messenger RNA level. Further, the basin stability analysis reveals the unexpectedly large basin of attraction for a hybrid-E/M phenotype. Finally, we identified mechanisms that can potentially elude the transition to a hybrid-E/M phenotype. Overall, our results unravel the early-warning signals that can be used to anticipate upcoming epithelial–hybrid-mesenchymal transitions. With the emerging evidence about the hybrid-E/M phenotype being a key driver of metastasis, drug resistance, and tumor relapse, our results suggest ways to potentially evade these transitions, reducing the fitness of cancer cells and restricting tumor aggressiveness.

scholarly journals Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

2015 ◽  
Vol 112 (32) ◽  
pp. 10056-10061 ◽  
Author(s):  
Lei Dai ◽  
Kirill S. Korolev ◽  
Jeff Gore
Keyword(s):  
Early Warning ◽  
Alternative Stable States ◽  
Basin Of Attraction ◽  
Environmental Drivers ◽  
Ecological Communities ◽  
Stable States ◽  
Warning Signals ◽  
Early Warning Signals ◽  
The Stability ◽  
Stability And Resilience

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.

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.

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 Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Gang Wang ◽  
Yuanyuan Li ◽  
Xiufen Zou
Keyword(s):  
Early Warning ◽  
Self Assembly ◽  
Disease Onset ◽  
Complex Diseases ◽  
Critical Threshold ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Development And Differentiation ◽  
Quantitative Changes

Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson’s correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.

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