Early Warning Signals For Critical Transitions Research Articles

Non-equilibrium early-warning signals for critical transitions in ecological systems

Complex systems can exhibit sudden transitions or regime shifts from one stable state to another, typically referred to as critical transitions. It becomes a great challenge to identify a robust warning sufficiently early that action can be taken to avert a regime shift. We employ landscape-flux theory from nonequilibrium statistical mechanics as a general framework to quantify the global stability of ecological systems and provide warning signals for critical transitions. We quantify the average flux as the nonequilibrium driving force and the dynamical origin of the nonequilibrium transition while the entropy production rate as the nonequilibrium thermodynamic cost and thermodynamic origin of the nonequilibrium transition. Average flux, entropy production, nonequilibrium free energy, and time irreversibility quantified by the difference in cross-correlation functions forward and backward in time can serve as early warning signals for critical transitions much earlier than other conventional predictors. We utilize a classical shallow lake model as an exemplar for our early warning prediction. Our proposed method is general and can be readily applied to assess the resilience of many other ecological systems. The early warning signals proposed here can potentially predict critical transitions earlier than established methods and perhaps even sufficiently early to avert catastrophic shifts.

Data-Driven Re-stabilization of Gene Regulatory Network Towards Early Medical Treatment

The Dynamical Network Biomarkers (DNBs) theory has been proposed to detect early-warning signals of critical transitions in gene regulatory networks only with High-Dimension Low-Sample-Size (HDLSS) data of the system state. Towards giving a theoretical foundation for early medical treatment, this paper proposes a data-driven approach for the re-stabilization of gene regulatory networks based on HDLSS data. In the proposed re-stabilization method, only the diagonal elements of the system matrix need to be adjusted. Namely, only the self-feedback loops of mRNA expression for genes are intervened in, which reduces the complexity of the early medical treatment based on gene regulation and makes it practical to be implemented. The proposed re-stabilization method is generalized to the systems with either saddle-node bifurcation or Hopf bifurcation. Numerical simulations have been implemented to validate the effectiveness of the proposed method.

Critical transitions in the hydrological system: early-warning signals and network analysis

Abstract. One critical challenge of studying Earth's hydroclimate system, in the face of global environmental changes, is to predict whether the system approaches a critical threshold. Here, we identified the critical transitions of hydrological processes, including precipitation and potential evapotranspiration, by analyzing their early-warning signals and system-based network structures. The statistical early-warning signals are manifest in increasing trends of autocorrelation and variance in the hydrologic system ranging from regional to global scales, prior to climate shifts in the 1970s and 1990s, in agreement with observations. We further extended the conventional statistics-based measures of early-warning signals to system-based network analysis in urban areas across the contiguous United States. The topology of an urban precipitation network features hub-periphery (clustering) and modular organization, with strong intra-regional connectivity and inter-regional gateways (teleconnection). We found that several network parameters (mean correlation coefficient, density, and clustering coefficient) gradually increased prior to the critical transition in the 1990s, signifying the enhanced synchronization among urban precipitation patterns. These topological parameters can not only serve as novel system-based early-warning signals for critical transitions in hydrological processes but also shed new light on structure–dynamic interactions in the complex hydrological system.

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

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.

Editorial — Tipping Points in Adaptive Capacity and Adaptation Processes

Journal of Extreme EventsVol. 04, No. 01, 1702002 (2017) Open AccessEditorial — Tipping Points in Adaptive Capacity and Adaptation ProcessesMatthias Garschagen and William SoleckiMatthias GarschagenRisk Management and Adaptive Planning (VARMAP), Institute for Environment and Human Security, United Nations University, UN Campus, Platz der Vereinten Nationen 1, D-53113 Bonn, GermanyCorresponding author and William SoleckiDepartment of Geography, Hunter College, City University of New York, 695 Park Avenue, Rm 1006 HN, New York, NY 10065, USAhttps://doi.org/10.1142/S234573761702002XCited by:4 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail References Haasnoot, M, Kwakkel JH, Walker WE and ter Maat J [2013] Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Global Environmental Change, 23 (2): 485–498. doi: https://doi.org/10.1016/j.gloenvcha.2012.12.006 Crossref, Google Scholar Kriegler, E, Hall, Jim W, Held, H, Dawson, R and Schellnhuber HJ [2009] Imprecise probability assessment of tipping points in the climate system. Proceedings of the National Academy of Sciences of the United States of America, 106 (13): 5041–5046. doi: https://doi.org/10.1073/pnas.0809117106 Crossref, Google Scholar Lenton, TM and Ciscar J-C [2013] Integrating tipping points into climate impact assessments. Climatic Change, 117 (3): 585–597. doi: https://doi.org/10.1007/s10584-012-0572-8 Crossref, Google Scholar Lenton, T, Held H, Kriegler E, Hall J, Lucht W, Rahmstorf S and Schellnhuber H-J [2008] Tipping elements in the Earth’s climate system. Proceedings of the National Academy of Sciences of the United States of America, 105 (6): 1786–1793. doi: https://doi.org/10.1073/pnas.0705414105 Crossref, Google Scholar Renaud, FG, Birkmann J, Damm M and Gallopín GC [2010] Understanding multiple thresholds of coupled social–ecological systems exposed to natural hazards as external shocks. Natural Hazards, 55 (3): 749–763. doi: https://doi.org/10.1007/s11069-010-9505-x Crossref, Google Scholar Scheffer, M [2009] Critical Transitions in Nature and Society. Princeton, NJ: Princeton University Press. Crossref, Google Scholar Scheffer, M, Bascompte J, Brock WA, Brovkin V, Carpenter SR and Dakos V [2009] Early-warning signals for critical transitions. Nature, 461 (7260): 53–59. doi: https://doi.org/10.1038/nature08227 Crossref, Google Scholar Schellnhuber, HJ and Martin M [2014] Climate-system tipping points and extreme wheather events. In Sustainable Humanity, Sustainable Nature: Our Responsibility Proceedings of a Joint Workshop, 2–6 May 2014, The Pontificial Academy of Science, Extra Series 41, Vatican. Online available at: http://www.casinapioiv.va/content/accademia/en/publications/extraseries/substainable.pdf. Google Scholar Solecki, W, Pelling M and Garsachagen M [2017] Transitions between risk management regimes in cities. Ecology and Society, 22 (2). doi: https://doi.org/10.5751/ES-09102-220238 Crossref, Google Scholar Werners, SE, Pfenninger S, van Slobbe E, Haasnoot M, Kwakkel JH and Swart RJ [2013] Thresholds, tipping and turning points for sustainability under climate change. Current Opinion in Environmental Sustainability, 5 (3–4): 334–340. doi: https://doi.org/10.1016/j.cosust.2013.06.005 Crossref, Google Scholar FiguresReferencesRelatedDetailsCited By 4A stepwise approach for identifying climate change induced socio-economic tipping pointsKees C.H. van Ginkel, Marjolijn Haasnoot and W.J. Wouter Botzen1 Jan 2022 | Climate Risk Management, Vol. 37Climate Change Adaptation as an Idea and a PracticeLeigh Glover and Mikael Granberg30 June 2020Identifying leverage points for strengthening adaptive capacity to climate changeLinda M. Rosengren, Christopher M. Raymond, Mila Sell and Hilkka Vihinen28 January 2021 | Ecosystems and People, Vol. 16, No. 1Designing a monitoring system to detect signals to adapt to uncertain climate changeMarjolijn Haasnoot, Susan van ’t Klooster and Jos van Alphen1 Sep 2018 | Global Environmental Change, Vol. 52 Recommended Vol. 04, No. 01 Metrics History Published: 30 November 2017 Information© The Author(s)This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.PDF download

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

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.

Identifying early-warning signals of critical transitions with strong noise by dynamical network markers.

Identifying early-warning signals of a critical transition for a complex system is difficult, especially when the target system is constantly perturbed by big noise, which makes the traditional methods fail due to the strong fluctuations of the observed data. In this work, we show that the critical transition is not traditional state-transition but probability distribution-transition when the noise is not sufficiently small, which, however, is a ubiquitous case in real systems. We present a model-free computational method to detect the warning signals before such transitions. The key idea behind is a strategy: “making big noise smaller” by a distribution-embedding scheme, which transforms the data from the observed state-variables with big noise to their distribution-variables with small noise, and thus makes the traditional criteria effective because of the significantly reduced fluctuations. Specifically, increasing the dimension of the observed data by moment expansion that changes the system from state-dynamics to probability distribution-dynamics, we derive new data in a higher-dimensional space but with much smaller noise. Then, we develop a criterion based on the dynamical network marker (DNM) to signal the impending critical transition using the transformed higher-dimensional data. We also demonstrate the effectiveness of our method in biological, ecological and financial systems.

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

One of the longest marine monitoring programs in the North Sea has been the spatiotemporal surveying of subsurface plankton since 1931. During this period a regime shift was detected in the late 1980s culminating in marked changes in phytoplankton, zooplankton and in the fisheries of horse mackerel. Here we used the phytoplankton colour index, a visual biomass estimate, from 1948 to 2010 and total diatom abundance from 1958 to 2010 to test whether the well-documented regime shift could have been anticipated by the recently developed Early-Warning Signals for critical transitions (EWS). We estimated EWS, namely autocorrelation and standard deviation, within moving windows along the time series prior to the regime shift. We found that both statistics increased revealing that the North Sea ecosystem was becoming progressively unstable prior to the regime shift. Moreover, this high-resolution time series permitted us to test for robustness, error and significance of the EWS. We did that by dividing the time series into independent blocks and estimating EWS after bootstrapping and randomising the blocks. This alternative approach confirmed the robustness of the EWS with limited associated errors. In particular, we found that the warning was significantly evident years before the onset of the regime shift. We conclude that EWS may provide robust and timely warning for upcoming regime shifts depending on the quality and quantity of recorded data in marine ecosystems.

Early-warning signals for critical transitions

Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

An issue to remember

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