biostudies-literatureClements CFEuropean Research Council10984https://www.ebi.ac.uk/biostudies/studies/S-EPMC4820807biostudies-literatureUnknown7Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait infoNature communicationsIncluding trait-based early warning signals helps predict population collapse.PMC4820807337785Clements CFOzgul AfalseIncluding trait-based early warning signals helps predict population collapse.Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait info2016-01-01T00:00:00Z2016 Mar2025-04-26T02:13:12.076Z2019-03-27T03:11:12ZS-EPMC48208072700996810.1038/ncomms10984