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

Ecosystem processes emerge from interactions among environmental conditions, individual behavior, fitness, and population dynamics. Energetics is a key mechanism underlying these linkages, yet energy budget models often lack an empirical basis for how suboptimal energy intake influences energy allocation to fitness-related processes. Here we introduce a Pattern-Informed Energetics (PIE) framework, which leverages diverse knowledge sources to infer key parameters governing vital but elusive energetic mechanisms. Using a rodent case study, we calibrated and evaluated the PIE framework against population dynamics, morphometrics, energetics, and life history patterns, assessing its ability to replicate an empirical experiment and applying it to future climate scenarios. We demonstrate its potential to mechanistically predict how environmental changes shape traits and population dynamics. By linking allocation dynamics to emergent patterns, PIE can enhance the integration of physiological insights into predictive models, advancing our understanding of system responses to environmental change while accounting for species' evolved life histories.