Detecting risk of regime shifts in ecosystems

2020 ◽  
Author(s):  
Juan Rocha
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Terrestrial Ecosystems ◽  
Regime Shifts ◽  
Critical Slowing Down ◽  
Small Disturbance ◽  
Slowing Down ◽  
Critical Transitions ◽  
The World ◽  
Universal Indicator

<div> <div> <div> <p>Ecosystems around the world are at riks of critical transitions due to increasing anthropogenic preasures and climate change. However, it is not clear where the risks are higher, or where ecosystems are more vulnerable. When a dynamic system is close to a threshold, it leaves a statistical signature on its time series known as critical slowing down. It takes longer to recover after a small disturbance, which translates into increases in variance, autocorrelation, and skewness or flickering. Here I measure critical slowing down on primary production proxies for marine and terrestrial ecosystems globally. Slowness is an indicator of potential instabilities and a proxy of resilience. While slowness is not a universal indicator for critical transitions, it can be used for detection of potential regime shifts.</p> </div> </div> </div>

scholarly journals Resilience indicators: prospects and limitations for early warnings of regime shifts

2015 ◽  
Vol 370 (1659) ◽  
pp. 20130263 ◽  
Author(s):  
Vasilis Dakos ◽  
Stephen R. Carpenter ◽  
Egbert H. van Nes ◽  
Marten Scheffer
Keyword(s):  
Early Warning ◽  
Temporal Correlation ◽  
Warning Signal ◽  
Regime Shifts ◽  
Tipping Points ◽  
Small Perturbations ◽  
Slowing Down ◽  
Critical Transitions ◽  
Wide Range ◽  
Resilience Indicators

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.

scholarly journals Transient indicators of tipping points in infectious diseases

2020 ◽  
Vol 17 (170) ◽  
pp. 20200094
Author(s):  
Suzanne M. O’Regan ◽  
Eamon B. O’Dea ◽  
Pejman Rohani ◽  
John M. Drake
Keyword(s):  
Infectious Diseases ◽  
Vaccine Uptake ◽  
Critical Slowing Down ◽  
Critical Threshold ◽  
Continuous Systems ◽  
Dominant Eigenvalue ◽  
Tipping Points ◽  
Critical Transition ◽  
Slowing Down ◽  
Critical Transitions

The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.

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

2019 ◽  
Author(s):  
Sukanta Sarkar ◽  
Sudipta Kumar Sinha ◽  
Herbert Levine ◽  
Mohit Kumar Jolly ◽  
Partha Sharathi Dutta
Keyword(s):  
Drug Resistance ◽  
Cell Fate ◽  
Early Warning ◽  
Cancer Metastasis ◽  
Critical Slowing Down ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Slowing Down ◽  
Tumor Relapse ◽  
Critical Transitions

AbstractIn the vicinity of a tipping point, critical transitions occur when small changes in an input condition causes 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 mRNA 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.Significance StatementEpithelial-hybrid-mesenchymal transitions play critical roles in cancer metastasis, drug resistance, and tumor relapse. Recent studies have proposed that cells in a hybrid epithelial/mesenchymal phenotype may be more aggressive than those on either end of the spectrum. However, no biomarker to predict upcoming transitions has been identified. Here, we show that critical slowing down based early warning signals can detect sudden transitions among epithelial, hybrid E/M, and mesenchymal phenotypes. Importantly, our results highlight how stable a hybrid E/M phenotype can be, and how can a transition to this state be avoided. Thus, our study provides valuable insights into restricting cellular plasticity en route metastasis.

scholarly journals A probabilistic risk assessment for the vulnerability of the European carbon cycle to extreme events: the ecosystem perspective

2014 ◽  
Vol 11 (6) ◽  
pp. 10167-10202
Author(s):  
S. Rolinski ◽  
A. Rammig ◽  
A. Walz ◽  
K. Thonicke ◽  
W. von Bloh ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Time Series ◽  
Meteorological Condition ◽  
Terrestrial Ecosystems ◽  
Probabilistic Risk Assessment ◽  
Meteorological Conditions ◽  
Border Region ◽  
Index Value ◽  
Probabilistic Risk

Abstract. Extreme meteorological events are most likely to occur more often with climate change, leading to a further acceleration of climate change through potentially devastating effects on terrestrial ecosystems. But not all extreme meteorological events lead to extreme ecosystem response. Unlike most current studies, we therefore focus on pre-defined hazardous ecosystem behaviour and the identification of coinciding meteorological conditions, instead of expected ecosystem damage for a pre-defined meteorological event. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and meteorological conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are, thus, estimated on the basis of observed hazardous ecosystem behaviour. We first adapt this generic approach to extreme responses of terrestrial ecosystems to drought and high temperatures, with defining the hazard as a negative net biome productivity over a 12 months period. Further, we show an instructive application for two selected sites using data for 1981–2010; and then apply the method on pan-European scale addressing the 1981–2010 period and future projections for 2071–2100, both based on numerical modelling results (LPJmL for ecosystem behaviour; REMO-SRES A1B for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI index to describe the meteorological condition. They also provide examples for their interpretation in case of vulnerability to drought for Spain with the expected value of the SPEI being 0.4 lower for hazardous than for non-hazardous ecosystem behaviour, and of non-vulnerability for Northern Germany, where the expected drought index value for hazard observations relates to wetter conditions than for the non-hazard observations. The pan-European assessment shows that significant results could be obtained for large areas within Europe. For 2071–2100 they indicate a shift towards vulnerability to drought, mainly in the central and north-eastern parts of Europe, where negative net biome productivity was not used to be associated with drought. In Southern parts of Europe, considerable vulnerability and risk to drought have been identified already under current conditions; in future, the difference in SPEI between hazardous and non-hazardous ecosystem behaviour as well as the frequency of hazardous ecosystem behaviour will increase further. Vulnerability decreased only for the border region between Ukraine, Russia and Belarus, where a change in ecosystem types occurred with less vulnerable plant species in the future. These first model-based applications indicate the conceptional advantages of the proposed method by focusing on the identification of critical meteorological conditions for which we observe hazardous ecosystem behaviour in the analysed dataset. Application of the method to empirical time series would be an important next step to test the methods.

scholarly journals Nonlinear time series analysis of palaeoclimate proxy records

2021 ◽  
Author(s):  
Norbert Marwan ◽  
Jonathan Donges ◽  
Reik Donner ◽  
Deniz Eroglu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Nonlinear Time Series ◽  
Regime Shifts ◽  
Nonlinear Regime ◽  
Nonlinear Time Series Analysis ◽  
Dynamical Regime ◽  
Series Analysis ◽  
Proxy Records ◽  
Critical Transitions

Identifying and characterising dynamical regime shifts, critical transitions or potential tipping points in palaeoclimate time series is relevant for improving the understanding of often highly nonlinear Earth system dynamics. Beyond linear changes in time series properties such as mean, variance, or trend, these nonlinear regime shifts can manifest as changes in signal predictability, regularity, complexity, or higher-order stochastic properties such as multi-stability.In recent years, several classes of methods have been put forward to study these critical transitions in time series data that are based on concepts from nonlinear dynamics, complex systems science, information theory, and stochastic analysis. These includeapproaches such as phase space-based recurrence plots and recurrence networks, visibility graphs, order pattern-based entropies, and stochastic modelling.Here, we review and compare in detail several prominent methods from these fields by applying them to the same set of marine palaeoclimate proxy records of African climate variations during the past 5~million years. Applying these methods, we observe notable nonlinear transitions in palaeoclimate dynamics in these marine proxy records and discuss them in the context of important climate events and regimes such as phases of intensified Walker circulation, marine isotope stage M2, the onset of northern hemisphere glaciation and the mid-Pleistocene transition. We find that the studied approaches complement each other by allowing us to point out distinct aspects of dynamical regime shifts in palaeoclimate time series.We also detect significant correlations of these nonlinear regime shift indicators with variations of Earth's orbit, suggesting the latter as potential triggers of nonlinear transitions in palaeoclimate.Overall, the presented study underlines the potentials of nonlinear time series analysis approaches to provide complementary information on dynamical regime shifts in palaeoclimate and their driving processes that cannot be revealed by linear statistics or eyeball inspection of the data alone.

scholarly journals Characterisation of Recent and Future Climatic Trends in the Region of Guelmim (Morocco)

2020 ◽  
Vol 150 ◽  
pp. 03021
Author(s):  
Said Amouch ◽  
Ahmed Akhssas ◽  
Lahcen Bahi ◽  
Rhita Bennouna
Keyword(s):  
Climate Change ◽  
Time Series ◽  
North Atlantic ◽  
Sustainable Management ◽  
Standardized Precipitation Index ◽  
Winter Precipitation ◽  
Intergovernmental Panel ◽  
Average Surface ◽  
Climatic Trends ◽  
The World

According to the Intergovernmental Panel on Climate Change (IPCC), climate change is manifested by the increase in average surface atmospheric temperatures and a decrease in rainfall. The impacts are multiple, complex and differentiated from one region to another in the world. In the Guelmim region (southern Morocco), climate change is manifested by severe droughts and/or recurrent floods. The objective of this study is to characterize the recent and future climate variability in the Guelmim region based on time series of precipitation, the study period goes from 1985 to 2017, and from 2020 to 2099 using Standardized Precipitation Index (SPI).Results of SPI analysis indicate that the most notable droughts for their varying intensity, duration and frequency occurred during the 1992-94 and 1997-2000 periods. Future analysis indicates the study area will face several extended periods of drought and wet during 2020 to 2099. The results of this study show also the link between North Atlantic Oscillation and winter precipitation in Guelmim, which are associated with the negative phase of NAO. The purpose of the study is to have a good management of crops and water resources in Guelmim region and either to insure a sustainable management of environment.

scholarly journals Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0144198 ◽  
Author(s):  
Vishwesha Guttal ◽  
Srinivas Raghavendra ◽  
Nikunj Goel ◽  
Quentin Hoarau
Keyword(s):  
Systemic Risk ◽  
Critical Slowing Down ◽  
Slowing Down ◽  
Critical Transitions

scholarly journals Spatial Correlation as an Early Warning Signal of Regime Shifts in a Multiplex Disease-Behaviour Network

2017 ◽  
Author(s):  
Peter C. Jentsch ◽  
Madhur Anand ◽  
Chris T. Bauch
Keyword(s):  
Spatial Correlation ◽  
Early Warning ◽  
Regime Shift ◽  
Tipping Point ◽  
Regime Shifts ◽  
Critical Slowing Down ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Network Layer ◽  
Slowing Down

AbstractEarly warning signals of sudden regime shifts are a widely studied phenomenon for their ability to quantify a system’s proximity to a tipping point to a new and contrasting dynamical regime. However, this effect has been little studied in the context of the complex interactions between disease dynamics and vaccinating behaviour. Our objective was to determine whether critical slowing down (CSD) occurs in a multiplex network that captures opinion propagation on one network layer and disease spread on a second network layer. We parameterized a network simulation model to represent a hypothetical self-limiting, acute, vaccine-preventable infection with shortlived natural immunity. We tested five different network types: random, lattice, small-world, scale-free, and an empirically derived network. For the first four network types, the model exhibits a regime shift as perceived vaccine risk moves beyond a tipping point from full vaccine acceptance and disease elimination to full vaccine refusal and disease endemicity. This regime shift is preceded by an increase in the spatial correlation in non-vaccinator opinions beginning well before the bifurcation point, indicating CSD. The early warning signals occur across a wide range of parameter values. However, the more gradual transition exhibited in the empirically-derived network underscores the need for further research before it can be determined whether trends in spatial correlation in real-world social networks represent critical slowing down. The potential upside of having this monitoring ability suggests that this is a worthwhile area for further research.

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