Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.

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) environment. Here, we present EWSmethods – an R package (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) 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.

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the initial emergence of disease outbreaks, offering hope that policy makers can make predictive rather than reactive management decisions. Here, using daily COVID-19 case data in combination with a novel, sequential analysis, we show that composite EWSs consisting of variance, autocorrelation, and return rate not only pre-empt the initial emergence of COVID-19 in the UK by 14 to 29 days, but also the following wave six months later. We also predict there is a high likelihood of a third wave as of the data available on 9th June 2021. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policy makers to improve the accuracy of time critical decisions based solely upon surveillance data.

Long held notions of the universally asocial octopus are being challenged due to the identification of high-density and interacting octopus populations in Australia, Indonesia, Japan and the deep sea. This study experimentally assessed the social tolerance and presence of potential prey items of Caribbean reef octopus, Octopus briareus, in a tropical marine lake (25°21′40″N, 76°30′40″W) on the island of Eleuthera, The Bahamas, by deploying artificial dens in multi-den groups or ‘units’ in the months of May and June 2019. Fifteen octopus were observed occupying dens (n = 100), resulting in 13 den units being occupied (n = 40). Two examples of adjacent occupation within a single den unit were identified but with zero examples of cohabitation/den sharing. Ecological models showed den and den unit occupation was predicted to increase with depth and differ between sites. Octopus also displayed no preference for isolated or communal units but preferred isolated dens over dens adjacent to others. Additionally, 47 % of occupied dens contained bivalve or crustacean items with no epifauna on their interior surface. The lack of epifauna suggests that these items have been recently ‘cleaned’ by occupying octopus and so represent likely prey. This study presents evidence of possible antisocial den use by O. briareus, a modification of the default ‘asocial’ ignoring of conspecifics typically attributed to octopus. This is likely in response to the high population density and may imply behavioural plasticity, making this system appropriate for further scrutiny as a research location on the influence of large, insular environments on marine species.