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Non-lethal injury in animals is both common and costly. The cost of regenerating autotomized limbs may leave less energy available for processes such as reproduction and growth, leading to trade-offs. Such trade-offs are context-dependent, and an individual’s energy allocation strategies may vary widely based on its condition and the environment. However, many traditional bioenergetics models have relied on fixed energy allocation rules, such as the -rule of dynamic energy budget theory, which assumes a fixed proportion (κ) of assimilated energy is always allocated to growth and maintenance. To determine whether incorporating optimality approaches into bioenergetics models improves the ability to predict energy allocation, we developed a dynamic state variable model that identifies optimal limb regeneration strategies in a model system, the Asian shore crabHemigrapsus sanguineus. Our model predictions align with known patterns for this species, including increased regeneration effort with injury severity, a shift from reproduction to growth as consumption amount increases, and an increase in regeneration effort as regeneration progresses. Lastly, Monte Carlo simulations of individuals from a previous experiment demonstrate that flexible energy allocation successfully predicts reproductive effort, suggesting that this approach may improve the accuracy of bioenergetics modeling.
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Technical note: A modified formulation of dynamic energy budget theory for faster computation of biological growth
Abstract. The mass conservation equation in the presence of boundary fluxes and chemical reactions from non-equilibrium thermodynamics is used to derive a modified dynamic energy budget (mDEB) model. Compared to the standard dynamic energy budget (sDEB) model (Kooijman, 2009), this modified formulation does not place the dilution effect in the mobilization kinetics of reserve biomass, and it maintains the partition principle for reserve mobilization dynamics for both linear and non-linear kinetics. Overall, the mDEB model shares most features with the sDEB model. However, for biological growth that requires multiple nutrients, the mDEB model is computationally much more efficient by not requiring numerical iterations for obtaining the specific growth rate. In an example of modeling the growth of Thalassiosira weissflogii in a nitrogen-limiting chemostat, the mDEB model was found to have almost the same accuracy as the sDEB model while requiring almost half of the computing time of the sDEB model. Since the sDEB model has been successfully applied in numerous studies, we believe that the mDEB model can help improve the modeling of biological growth and the associated ecosystem processes in various contexts.
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- Award ID(s):
- 2125069
- PAR ID:
- 10636701
- Editor(s):
- Middelburg, Jack
- Publisher / Repository:
- European Geosciences Union
- Date Published:
- Journal Name:
- Biogeosciences
- Volume:
- 22
- Issue:
- 7
- ISSN:
- 1726-4189
- Page Range / eLocation ID:
- 1809 to 1819
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/bg-22-1809-2025
