Predicting adaptation and evolution of plasticity from temporal environmental change

Environmental change can drive evolutionary adaptation and determine geographic patterns of biodiversity. Yet at a time of rapid environmental change, our ability to predict its evolutionary impacts is incomplete. Temporal environmental change, in particular, involves a combination of major components such as abrupt shift, trend, cyclic change, and noise. Theoretical predictions exist for adaptation to isolated components, but knowledge gaps remain regarding their joint impacts. We extend classic evolutionary theory to develop a model for the evolution of environmental tolerance by the evolution of an underlying developmentally plastic trait, in response to major components of temporal change. We retrieve and synthesise earlier predictions of responses to isolated components, and generate new predictions for components changing simultaneously. Notably, we show how different forms of environmental predictability emerging from the interplay of cyclic change, stochastic change (noise) and lag between development and selection shape predictions. We then illustrate the utility of our model for generating testable predictions for the evolution of adaptation and plasticity when parameterised with real time series data. Specifically, we parameterise our model with daily time series of sea-surface temperature from a global marine hotspot in southern Australia, and use model simulations to predict the evolution of thermal tolerance, and geographic differences in tolerance, in this region. By synthesising theory on evolutionary adaptation to temporal environmental change, and providing new insights into the joint effects of its different components, our framework, embedded in a Shiny app, offer a path to better predictions of biological responses to climate change.