EWSmethods - an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models

Aug 6, 2026

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EWSmethods introduces an open-source R package designed to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning. The package unifies classical and state-of-the-art EWS approaches for both univariate and multivariate time series, supporting rolling and expanding window calculations. It also integrates an interface to the Python model EWSNet, which estimates the probability of sudden tipping or smooth transitions. The article motivates the software and explains its role in resilience assessment through accompanying tutorials, vignettes, and FAQs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Background

Critical slowing down and tipping points

Detecting early warning signals via summary statistics

EWSmethods software note

Unified EWS methods for univariate and multivariate series

Rolling vs expanding window calculations

Integration with EWSNet (machine learning)

Tutorials, vignettes, and FAQs

O'brien, D. A. , Deb, S. , Sidheekh, S. , Krishnan, N. C. , Sharathi dutta, P. , & Clements, C. F. (2023) . EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models. Ecography, 2023(10), Article e06674 . Advance online publication. [https://doi.org/10.1111/ecog.06674](https://doi.org/10.1111/ecog.06674) Publisher's PDF, also known as Version of record License (if available): CC BY Link to published version (if available): 10.1111/ecog.06674 Link to publication record on the Bristol Research Portal PDF-document This is the final published version of the article (version of record) . It first appeared online via Wiley at [https://doi.org/10.1111/ecog.06674 . Please](https://doi.org/10.1111/ecog.06674 . Please) refer to any applicable terms of use of the publisher. University of Bristol – Bristol Research Portal General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: [http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/](http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/) ECOGRAPHY Software note EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models Duncan A. O’Brien✉1, Smita Deb2, Sahil Sidheekh3, Narayanan C. Krishnan4, Partha Sharathi Dutta2 and Christopher F. Clements1 1School of Biological Sciences, University of Bristol, Bristol, UK 2Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India 3Department of Computer Science, The University of Texas, Dallas, TX, USA 4Department of Data Science, Indian Institute of Technology Palakkad, Kozhippara, Kerala, India Correspondence: Duncan A. O’Brien ([duncan.a.obrien@gmail.com](duncan.a.obrien@gmail.com)) Ecography 2023: e06674 doi: 10.1111/ecog.06674 Subject Editor: F. Guillaume Blanchet Editor-in-Chief: Miguel Araújo Accepted 1 June 2023 [www.ecography.org](www.ecography.org) Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point, and have been applied in fields including ecology, epidemiology, economics, and physics. This potential universality has led to the development of a suite of computational approaches aimed at improving the reliability of these methods. Classic methods based on univariate data have a long history of use, but recent theoretical advances have expanded EWSs to multivariate datasets, particularly relevant given advancements in remote sensing. More recently, novel machine learning approaches have been developed but have not been made accessible in the R ([www.r-project.org](www.r-project.org)) environment. Here, we present EWSmethods – an R package ([www.r-project.org](www.r-project.org)) that provides a unified syntax and interpretation of the most popular and cutting edge EWSs methods applicable to both univariate and multivariate time series. EWSmethods provides two primary functions for univariate and multivariate systems respectively, with two forms of calculation available for each: classical rolling window time series analysis, and the more robust expanding window. It also provides an interface to the Python machine learning model EWSNet which predicts the probability of a sudden tipping point or a smooth transition, the first of its form available to R ([www.r-project.org](www.r-project.org)) users. This note details the rationale for this open-source package and delivers an introduction to its functionality for assessing resilience. We have also provided vignettes and an external website to act as further tutorials and FAQs. Keywords: bifurcation, critical, ecosystem management, ecosystem, resilience, time series, transition © 2023 Th

FAQs

What problem does EWSmethods address?

It provides computational tools to forecast tipping points by using early warning signals and resilience measures, including support for machine learning approaches.

What data types and methods does the package support?

EWSmethods covers both univariate and multivariate time series and offers classical rolling window analysis as well as a more robust expanding window approach.

How does EWSmethods use machine learning?

It provides an interface to the Python machine learning model EWSNet to predict the probability of a sudden tipping point or a smooth transition.

Aug 6, 2026

Upload by Logic