Evaluating the performance of multivariate indicators of resilience loss

Abstract Various complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing...

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Autores principales:	Els Weinans, Rick Quax, Egbert H. van Nes, Ingrid A. van de Leemput
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2df28a83296048f089cbebea4bf648e1
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spelling	oai:doaj.org-article:2df28a83296048f089cbebea4bf648e12021-12-02T13:41:44ZEvaluating the performance of multivariate indicators of resilience loss10.1038/s41598-021-87839-y2045-2322https://doaj.org/article/2df28a83296048f089cbebea4bf648e12021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87839-yhttps://doaj.org/toc/2045-2322Abstract Various complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.Els WeinansRick QuaxEgbert H. van NesIngrid A. van de LeemputNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Els Weinans Rick Quax Egbert H. van Nes Ingrid A. van de Leemput Evaluating the performance of multivariate indicators of resilience loss
description	Abstract Various complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.
format	article
author	Els Weinans Rick Quax Egbert H. van Nes Ingrid A. van de Leemput
author_facet	Els Weinans Rick Quax Egbert H. van Nes Ingrid A. van de Leemput
author_sort	Els Weinans
title	Evaluating the performance of multivariate indicators of resilience loss
title_short	Evaluating the performance of multivariate indicators of resilience loss
title_full	Evaluating the performance of multivariate indicators of resilience loss
title_fullStr	Evaluating the performance of multivariate indicators of resilience loss
title_full_unstemmed	Evaluating the performance of multivariate indicators of resilience loss
title_sort	evaluating the performance of multivariate indicators of resilience loss
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2df28a83296048f089cbebea4bf648e1
work_keys_str_mv	AT elsweinans evaluatingtheperformanceofmultivariateindicatorsofresilienceloss AT rickquax evaluatingtheperformanceofmultivariateindicatorsofresilienceloss AT egberthvannes evaluatingtheperformanceofmultivariateindicatorsofresilienceloss AT ingridavandeleemput evaluatingtheperformanceofmultivariateindicatorsofresilienceloss
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Evaluating the performance of multivariate indicators of resilience loss

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