Pattern-informed energetics: Energy allocation modeling for predicting trait variation and population persistence

Ecosystem processes emerge from complex interactions between environmental conditions, individual behavior, fitness, and population dynamics. A central mechanism driving these relationships is energetics, yet many energy budget models lack an empirical foundation for how organisms allocate energy when resources are limited. Without accounting for real-world variability in energy use, these models may oversimplify the links between environmental change, individual performance, and population outcomes. Here, we introduce the Pattern-Informed Energetics (PIE) framework, a novel approach that leverages diverse empirical data sources to infer key parameters governing energy allocation. Using a rodent case study, we rigorously calibrated and evaluated PIE against multiple observed patterns, including in population dynamics, morphometrics, energetics, and life history traits, assessing its ability to replicate experimental results and predict responses to future climate scenarios. Our findings demonstrate that PIE can mechanistically predict how environmental change shapes traits and population trajectories, offering a powerful tool for improving biodiversity forecasting. By linking energy allocation to emergent ecological patterns, PIE enhances the integration of physiological insights into predictive models, helping to advance our understanding of species’ responses to environmental change while accounting for their evolved life histories.