Multivariate forms of EWSs (Weinans et al. 2019, Lever et al. 2020, Medeiros et al. 2022) and deep learning models are of particular interest as they appear superior tools to the univariate signals described above. Multivariate approaches exploit information from multiple measurements of a shared system (e.g. multiple species in an ecosystem or multiple sensors in a combustion engine) to provide an overall signal of system resilience. Pooling information in this way buffers against the uncertainty of choosing which data source should be assessed. For example, the trophic level of EWS assessment influences the strength of signal observed in simulated communities (Patterson et al. 2021), and whilst the authors provide guidance on the optimum species/time series to monitor, the required information to identify those time series may not be available to empirical users. Multivariate EWSs can therefore provide a naïve yet robust assessment for multivariate data in the absence of complete information.
Similarly, CSD may not be the only signature of systems close to a tipping point. Our identification of the phenomenon stems from linear stability analysis (LSA) of mathematical models (Ludwig et al. 1978, Scheffer et al. 2009), but machine learning tools can identify other phenomenological features not detected from LSA. For example, machine learning models trained upon transitioning data outperform equivalent models trained upon the EWSs of the same transitioning data (Deb et al. 2022). This is indicative of alternative features being more informative than CSD to warrant general usage, although the ‘black-box’ nature of the approach limits its accountability (Enni and Herrie 2021). This being said, multiple machine learning models are now available for transitioning systems that improve the transparency of predictions by training on simple mathematical models associated with LSA (Bury et al. 2021, Deb et al. 2022). These models can consequently build upon our foundational knowledge of tipping points by taking advantage of the biases inherent in their training.
Currently, neither multivariate nor machine learning approaches have functionality for R users and resultingly there is a need for simple tools to interact with the variety of EWS approaches available to researchers. Certain EWS functionality has previously been provided by the earlywarnings R package , however the package is limited to one form of EWS calculation (rolling windows) in univariate data only. There have also been advances using alternative methodologies such as expanding window and composite EWSs, which introduce data in an add-one-in fashion to provide a standardised time series of EWS strength . This second approach improves the reliability of EWS predictions in univariate data but is not currently available in an easy-to-use form. Unfortunately, many of the custom functions written to facilitate this research are limited to the subscription MATLAB product or hidden in publications’ supplementary information . In combination, this has limited the accessibility of EWS development to the wider community.
Compiling these various functions in to a single and comprehensive R package whilst rectifying computational errors is required to increase reproducibility of empirical ecological tipping point research and improve the interpretation and visualisation of results. We therefore designed the EWSmethods  R package to provide a suite of ‘user-friendly’ functions to predict critical transitions across both univariate and multivariate data sources and provide interpretable graphics. For univariate data, such as local fisheries or country level disease cases, EWSs can be estimated using either the rolling window approach of earlywarnings or the expanding window approach of Drake and Griffen (2010). The package also provides the user the capability to query the Python based EWSNet deep learning model in the R environment and generate predictions on the time series’ future. And finally, if multiple measurements have been made of a single system – such as when monitoring multiple species in the same community – multivariate EWSs can be estimated using either rolling or expanding window approaches. EWSmethods therefore represents a compilation of new and existing tools to support this expanding field in an easy to use and interpret form. A comparison of the features EWSmethodsprovides versus the currently available earlywarnings package is provided in Table 1.