Do early warning indicators consistently predict nonlinear change in long‐term ecological data?

Summary Anthropogenic pressures, including climate change, are causing nonlinear changes in ecosystems globally. The development of reliable early warning indicators ( EWI s) to predict these changes is vital for the adaptive management of ecosystems and the protection of biodiversity, natural capital and ecosystem services. Increased variance and autocorrelation are potential early warning indicators and can be readily estimated from ecological time series. Here, we undertook a comprehensive test of the consistency between early warning indicators and nonlinear abundance change across species, trophic levels and ecosystem types. We tested whether long‐term abundance time series of 55 taxa (126 data sets) across multiple trophic levels in marine and freshwater ecosystems showed (i) significant nonlinear change in abundance ‘turning points’ and (ii) significant increases in variance and autocorrelation (‘early warning indicators’). For each data set, we then quantified the prevalence of three cases: true positives (early warning indicators and associated turning point), false negatives (turning point but no associated early warning indicators) and false positives (early warning indicators but no turning point). True positives were rare, representing only 9% (16 of 170) of cases using variance, and 13% (19 of 152) of cases using autocorrelation. False positives were more prevalent than false negatives (53% vs. 38% for variance; 47% vs. 40% for autocorrelation). False results were found in every decade and across all trophic levels and ecosystems. Time series that contained true positives were uncommon (8% for variance; 6% for autocorrelation), with all but one time series also containing false classifications. Coherence between the types of early warning indicators was generally low with 43% of time series categorized differently based on variance compared to autocorrelation. Synthesis and applications . Conservation management requires effective early warnings of ecosystem change using readily available data, and variance and autocorrelation in abundance data have been suggested as candidates. However, our study shows that they consistently fail to predict nonlinear change. For early warning indicators to be effective tools for preventative management of ecosystem change, we recommend that multivariate approaches of a suite of potential indicators are adopted, incorporating analyses of anthropogenic drivers and process‐based understanding.