2020
DOI: 10.1098/rsos.200896
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Data-driven identification of reliable sensor species to predict regime shifts in ecological networks

Abstract: 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 pro… Show more

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Cited by 13 publications
(16 citation statements)
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“…First, there exist a probability distribution over the possible values of Kendall's τ corresponding to each measurement. Hence, a positive Kendall's τ does not guarantee that the system is moving toward a transition unless its significance is confirmed [13,15,16]. Even for a signal measured from a stationary system, one might obtain a positive value for Kendall's τ .…”
Section: Introductionmentioning
confidence: 99%
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“…First, there exist a probability distribution over the possible values of Kendall's τ corresponding to each measurement. Hence, a positive Kendall's τ does not guarantee that the system is moving toward a transition unless its significance is confirmed [13,15,16]. Even for a signal measured from a stationary system, one might obtain a positive value for Kendall's τ .…”
Section: Introductionmentioning
confidence: 99%
“…In addition, identifying the trend of early warning signals might not be trivial due to stochastic fluctuations in the reported values of early warning signals over time. As a result, Kendall's τ coefficient is often used to quantify the trend of statistics related to the critical slowing down phenomenon [8,13]. Kendall's τ is a measure of the correlation between the rank order of the observed values and their order in time [14].…”
Section: Introductionmentioning
confidence: 99%
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“…As a result, developing simple and tractable data-driven system identification methods via a limited number of recorded observations has been the motivation of recent research. These methods focus on the discovery of dynamical systems from high-dimensional data [46][47][48][49][50][51][52][53][54][55] and making predictions of system dynamics based on the identified model. Examples include data-driven identification methods based on nonlinear regression [56], empirical dynamic modelling [57], normal form methods [58], nonlinear Laplacian spectral analysis [59], eigensystem realization algorithms [60], dynamic mode decomposition (DMD) [52,54] and artificial neural networks [61,62].…”
Section: Introductionmentioning
confidence: 99%
“…Data-driven prediction methods have also offered a solution to the formidable challenge of predicting catastrophic events in a variety of complex systems. Recent studies have shown that features extracted from data can be used to predict critical transitions [165] and extreme events [166] in the dynamics of a variety of complex systems, including aeroelastic systems [39,40,44,167,168], ecological systems [51,[169][170][171][172][173][174][175], epidemiological systems [176][177][178][179], traffic flow systems [158,180] and fluid flows [166,[181][182][183].…”
Section: Introductionmentioning
confidence: 99%