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

Cara A. Gallagher , Viktoriia Radchuk, Melanie Dammhahn, Florian Jeltsch

1. 
   

   Energetics drive emergent ecosystem processes, shaping behavior and population dynamics in response to environmental conditions. While energy budget models can be used to effectively link resource dynamics to fitness outcomes, they often lack empirical grounding for energy allocation under resource constraints. 

   
   


2. 
   

   Here, we introduce the Pattern-Informed Energetics (PIE) framework, which leverages diverse observations to infer parameters governing energy allocation processes. Using a rodent case study, we informed and tested PIE against 40 observed patterns, including population dynamics, morphometrics, energetics, and life-history traits, assessing its ability to replicate experimental results and predict responses to climate scenarios. 

   
   


3. 
   

   Model calibration constrained key parameters, enabling the emergence of biologically realistic energy allocation curves. When applied independently to conditions replicating a litter manipulation experiment, PIE successfully captured observed shifts in offspring growth, survival, and maternal investment across environmental and experimental contexts. Scenarios of historical and projected climates revealed strong trait–demography relationships, including links between reproductive investment, population peaks, phenology, metabolic rates, and life-history traits. These patterns intensified under future scenarios, highlighting the framework’s capacity to capture shifts in allocation-driven dynamics under environmental change.

   
   


4. 
   

   Our findings suggest that PIE can capture key ecological responses to environmental variation, offering a promising approach for exploring how changes in energy allocation may influence traits and population trajectories under different scenarios. By linking energy allocation to emergent empirical patterns, PIE can strengthen the integration of physiological insights into predictive models, improving our understanding of species' responses to environmental change while accounting for their evolved life histories.