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by claude@2026-07, 2026-07-08
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The preprint introduces Pattern-Informed Energetics (PIE), a modeling framework that infers energy allocation strategies to survival, growth, and reproduction from empirical data using inverse parameterization rather than fixed optimization assumptions. PIE was applied both cross-species across six mammals covering a 1,000-fold body-mass range and in a spatially explicit population model of the bank vole, where it reproduced field and experimental patterns including seasonal dynamics and how litter manipulation altered reproductive costs and maternal survival. The cross-species analysis found strong allometric scaling in allocation midpoints, with larger mammals reallocating energy toward growth and reproduction at higher body conditions, but the steepness of the energetic response was harder to infer and is a key limitation they note regarding needed data. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
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.
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This is a Preprint and has not been peer reviewed. This is version 7 of this Preprint.
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Energy allocation among survival, growth, and reproduction is central to population dynamics, yet remains difficult to quantify directly. Traditional models often rely on fixed rules or optimization assumptions that may not capture real-world variability. We introduce Pattern‑Informed Energetics (PIE), a framework that infers allocation strategies from empirical data using inverse parameterization. We applied PIE at two scales: a cross-species study of six mammals spanning a 1,000-fold mass range and a spatially explicit population model of the bank vole (Myodes glareolus). Cross-species results revealed strong allometric scaling in allocation midpoints, with larger mammals shifting energy to growth and reproduction at higher body conditions. In the bank vole model, PIE reproduced complex field and experimental patterns, including seasonal dynamics and the effects of litter manipulation on reproductive costs and maternal survival. While life-history patterns effectively constrained allocation midpoints, the steepness of the energetic response was more difficult to infer, highlighting specific data needs for future studies. PIE provides a flexible, transparent approach to bioenergetic modeling, quantifying uncertainty and revealing how evolved life-history strategies emerge from energetic constraints, thereby improving predictions of species’ responses to environmental change.
https://doi.org/10.32942/X20W6V
Ecology and Evolutionary Biology, Life Sciences, Physiology
Published: 2025-03-07 16:47
Last Updated: 2026-05-19 20:01
- Version 6 - 2026-05-13
- Version 5 - 2025-05-28
- Version 4 - 2025-04-16
- Version 3 - 2025-04-16
- Version 2 - 2025-04-16
- Version 1 - 2025-03-07
CC BY Attribution 4.0 International
Data and Code Availability Statement:
All data, code, and materials used in the analyses are made available for download on Figshare at: Gallagher, Cara (2025). Pattern-informed energetics: Energy allocation modeling for predicting trait variation and population persistence. figshare. Dataset. https://doi.org/10.6084/m9.figshare.28390238.v1
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English
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