"A major goal in biodiversity conservation is to predict responses of biological populations to environmental change. To achieve this, we must identify early warning signals of the demographic changes that underlie population declines. Some studies have achieved phenomenological prediction of sudden changes, but recent advances that link trait-based information with demography hint that a mechanistic understanding is within reach. I propose to develop a predictive framework by identifying the demographic and phenotypic statistics that can be used as early warning signals of demographic regime shifts. I have investigated links between ecological and evolutionary processes in changing environments for many years, and I will now build on this experience to develop a predictive theory. First, we will analyse unique long-term individual-based datasets from nine mammal species. The species represent a continuum of life histories and environmental conditions, and some of them show population dynamic regime shifts. Second, we will construct trait-based demographic models of each system and perturb key parameters to simulate population and trait dynamics under multiple environmental scenarios. The simulations will yield time-series data from which we will estimate demographic and phenotypic statistics. We will test the ability of these statistics to predict demographic changes using a novel decision algorithm framework. Finally, using two laboratory microcosms, we will experimentally test the ability of these statistics to predict population responses to environmental change. This project will exploit nine unique natural systems to identify early warning signals of population change and test these on two experimental systems. Results will provide much-needed predictive insight into how wildlife populations respond to environmental change, and will be of highest importance to management of wildlife populations whether they are of conservation concern, invasive, or exploitable."