Contemporary rates of biodiversity decline emphasize the need for reliable ecological forecasting, but cur-rent methods vary in their ability to predict the declines of real-world populations. Acknowledging that stress acts at the individual level, and that it is the sum of these individual-level effects which drives popu-lations to collapse, shifts the focus of predictive ecology away from using predominantly abundance data. Doing so opens new opportunities to develop predictive frameworks which utilize increasingly available multi-dimensional data which have previously been overlooked for ecological forecasting. Using this ra-tional, we propose that stressed populations will exhibit a predictable sequence of detectable changes through time: (i) changes in individuals’ behaviour will occur as the first sign of increasing stress, followed by (ii) changes in fitness related morphological traits, (iii) shifts in the dynamics (e.g. birth rates) of popu-lations, and finally (iv) abundance declines. We discuss how monitoring the sequential appearance of these signals supplies information to discern whether a population becoming increasingly stressed risks collapse or is adapting in the face of environmental change. Such a timeline of signals provides a new framework to implement forecasting methods combining multidimensional data (e.g. behaviour, morphology, abun-dance) that may increase the ability to predict population collapse.

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

Connectivity maintains the spatial dynamics of metapopulations by promoting dispersal between habitat patches, potentially buffering populations and communities against continued global change. However, this function is threatened by habitats becoming increasingly fragmented, and habitat matrices becoming increasingly inhospitable, potentially reducing the resilience and persistence of populations. Yet, we lack a clear understanding of how reduced connectivity interacts with rates of environmental change to destabilise populations. Using laboratory microcosms containing metapopulations of the Collembola Folsomia candida, we investigate the impact of habitat connectivity on metapopulation persistence under a range of simulated droughts, a key stressor for this species. We manipulated both drought severity and the number of patches affected by drought across landscapes connected by either good or poor-quality corridors. We measured the time of population extinction, the maximum rate of population decline, and the variability of abundance among patches as criteria to evaluate the persistence ability of metapopulations. We show that whilst drought severity and number of drought-affected patches negatively influenced population persistence, these results were mitigated by increased habitat connectivity, which increased population persistence time and decreased both how fast populations declined and the variability in abundance among patches. Our results suggest that enhancing spatial connectivity can increase the persistence of metapopulations, increasing the time available for conservation actions to take effect, and/or for species to adapt or move in the face of continued stress. Given that fragmentation increases the isolation of habitats, improving habitat connectivity by using good quality corridors may provide a useful strategy to enhance the resistance of spatially structured populations.

High resolution monitoring is fundamental to understand and predict the dynamics of ecological communities in an era of global change and biodiversity declines. While real-time and fully automated monitoring of the abiotic components of ecosystems has been possible for some time, monitoring the biotic components at different organizational scales, e.g. from individual behaviours and traits to the abundance and distribution of species, is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which are able to extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological populations and communities via such technologies is still in its infancy, being primarily achieved at low spatiotemporal resolutions within specific stages of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify, and count multiple species, and even record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology and conservation: the ability to rapidly generate high resolution, multidimensional, and critically, standardized data across complex ecologies.

Understanding the dynamics of small populations is critical to conserve those species at most risk. Previous work has identified demographic and environmental factors that can mutually reinforce one-another to drive populations rapidly to extinction – a process known as the ‘extinction vortex.’ However, studies investigating robustness to the extinction vortex in relation to life history and ecological traits have been lacking. Here, we assemble a database of 55 vertebrate populations monitored to extirpation and perform three analyses to investigate whether a key fitness-related phenotypic trait – body size – influences the rate at which populations succumb to the extinction vortex. We find evidence that populations of smaller-bodied species deteriorate at a faster rate, suggesting that intrinsic biological traits can alter the susceptibility of species to the extinction vortex, and may serve as a useful feature for prioritizing which populations to invest conservation effort in.