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.
