Systematically false positives in early warning signal analysis

Georg Jäger,Manfred Füllsack,Hideyuki Doi

doi:10.1371/journal.pone.0211072

Abstract

Many systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic shifts in ecosystems. In order to predict imminent critical transitions, a mathematical apparatus called early warning signals has been developed and this method is used successfully in many scientific areas. However, not all critical transitions can be detected by this approach (false negative) and the appearance of early warning signals does not necessarily proof that a critical transition is imminent (false positive). Furthermore, there are whole classes of systems that always show early warning signals, even though they do not feature critical transitions. In this study we identify such classes in order to provide a safeguard against a misinterpretation of the results of an early warning signal analysis of such systems. Furthermore, we discuss strategies to avoid such systematic false positives and test our theoretical insights by applying them to real world data.

Highlights

Data Availability Statement: Data are located at Figshare:
In this study we were able to show that many different systems are prone to false positives in early warning signals (EWSs) analysis, when investigating raw data
Among them exponentially growing systems, systems featuring logistic growth and systems experiencing logarithmic decay, can inherently show an increase in standard deviation as well as autocorrelation at lag 1, which is usually interpreted as an EWS [11]

Summary

Introduction

Data Availability Statement: Data are located at Figshare: (https://figshare.com/articles/falseEWS_ rar/7472099). Critical transitions are an important feature in many systems, in which the state of a system changes abruptly and a return to the previous state becomes difficult or even impossible Such transitions can be observed in medicine [1,2,3], climate science [4, 5], economics [6] and ecology [7,8,9]. It is possible to find EWSs in systems that will not show a critical transition This is called a false positive and is especially problematic when used on statistical data in order to predict critical transitions in the future. In that case it is impossible to differentiate between a true and a false positive, since no data from future points in time are available to verify whether a critical transition follows the EWS or not. Numeric details on the investigated systems can be found in Appendix A

Methods

Results

Discussion