Systematic analysis and optimization of early warning signals for critical transitions using distribution data.

[en] Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.

Disciplines :

Life sciences: Multidisciplinary, general & others

Author, co-author :

PROVERBIO, Daniele ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Imaging AI > Team Andreas HUSCH ; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QL, UK

SKUPIN, Alexander ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Integrative Cell Signalling ; National Center for Microscopy and Imaging Research, University of California San Diego, Gilman Drive, La Jolla, CA 9500, USA

GONCALVES, Jorge ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control ; Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK

External co-authors :

Language :

English

Title :

Systematic analysis and optimization of early warning signals for critical transitions using distribution data.

Publication date :

21 July 2023

Journal title :

iScience

eISSN :

2589-0042

Publisher :

Elsevier Inc., United States

Volume :

Issue :

Pages :

107156

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S2589004223012336?httpAccept=text/xml

Funding text :

The authors would thank their colleagues for valuable discussions. D.P.’s work is supported by the FNR PRIDE DTU CriTiCS , ref. 10907093 , and A.S. by the FNR ( C14/BM/7975668/CaSCAD ) and by the NIH NBCR ( NIH P41 GM103426 ).

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