A general model for the evolution of thermal performance curves with application to real time-series data

Thermal performance curves (TPCs) are widely used to investigate the effect of changes in body temperature on an organisms performance. Despite empirical evidence that temperature-dependent performance is ubiquitous across taxa, the field lacks models for how thermal performance evolves under realistic timeseries, genetic architectures, and physiological constraints. We address this gap by integrating a mathematical model with individual-based quantitative population genetic simulations. Our model can predict the evolutionary trajectory and shape of TPCs for any given thermal regime. Our model reproduces core properties of TPC evolution from previous studies such as the emergence of generalists in variable environments, but also explains why organisms may evolve TPCs that do not match their historical body temperature range. We uncover novel dynamics of adaptive tracking, the most notable being multi-generation lags between temperature and TPC parameters that can lead to unexpected correlations between the two. Our model predicts empirically observed patterns of adaptive tracking of critical thermal minimum in the invasive pest Drosophila suzukii, including individual-level variability and multi-generation lags with changing temperature. Our results also highlight the limitations of models that ignore factors that influence TPC evolution and individual variability, such as autocorrelation in temperature timeseries, effective population size, evolution of additive genetic correlation in TPC parameters, genetic architecture, and physiological constraints. Our flexible simulation model can incorporate these factors and help generate empirically testable hypotheses of how species will evolve in response to global climate change.