Abstract
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.