scholarly journals Structure-based identification of sensor species for anticipating critical transitions

2021 ◽  
Vol 118 (51) ◽  
pp. e2104732118
Author(s):  
Andrea Aparicio ◽  
Jorge X. Velasco-Hernández ◽  
Claude H. Moog ◽  
Yang-Yu Liu ◽  
Marco Tulio Angulo
Keyword(s):  
Early Warning ◽  
Ecological Systems ◽  
System Structure ◽  
Warning Signals ◽  
Large Dataset ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Plant Animal Interactions ◽  
Multispecies Systems ◽  
Plant Pollinator

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure—that is, the network topology of plant–animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of “sensor species,” whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant–pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

Alternative stable states, tipping points, and early warning signals of ecological transitions

2020 ◽  
pp. 263-284
Author(s):  
John M. Drake ◽  
Suzanne M. O’Regan ◽  
Vasilis Dakos ◽  
Sonia Kéfi ◽  
Pejman Rohani
Keyword(s):  
Early Warning ◽  
Alternative Stable States ◽  
Intrinsic Noise ◽  
Point Theory ◽  
Ecological Systems ◽  
Tipping Point ◽  
Stable States ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Theoretical Predictions

Ecological systems are prone to dramatic shifts between alternative stable states. In reality, these shifts are often caused by slow forces external to the system that eventually push it over a tipping point. Theory predicts that when ecological systems are brought close to a tipping point, the dynamical feedback intrinsic to the system interact with intrinsic noise and extrinsic perturbations in characteristic ways. The resulting phenomena thus serve as “early warning signals” for shifts such as population collapse. In this chapter, we review the basic (qualitative) theory of such systems. We then illustrate the main ideas with a series of models that both represent fundamental ecological ideas (e.g. density-dependence) and are amenable to mathematical analysis. These analyses provide theoretical predictions about the nature of measurable fluctuations in the vicinity of a tipping point. We conclude with a review of empirical evidence from laboratory microcosms, field manipulations, and observational studies.

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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