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Abstract Stress resilience is defined as the ability to rebound to a homeostatic state after exposure to a perturbation. Organisms modulate various physiological mediators to respond to unpredictable changes in their environment. The gut microbiome is a key example of a physiological mediator that coordinates a myriad of host functions including counteracting stressors. Here, we highlight the gut microbiome as a mediator of host stress resilience in the framework of the reactive scope model. The reactive scope model integrates physiological mediators with unpredictable environmental changes to predict how animals respond to stressors. We provide examples of how the gut microbiome responds to stressors within the four ranges of the reactive scope model (i.e., predictive homeostasis, reactive homeostasis, homeostatic overload, and homeostatic failure). We identify measurable metrics of the gut microbiome that could be used to infer the degree to which the host is experiencing chronic stress, including microbial diversity, flexibility, and gene richness. The goal of this perspective piece is to highlight the underutilized potential of measuring the gut microbiome as a mediator of stress resilience in wild animal hosts.
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A mathematical representation of the reactive scope model
Abstract Researchers have long sought to understand and predict an animal’s response to stressful stimuli. Since the introduction of the concept of homeostasis, a variety of model frameworks have been proposed to describe what is necessary for an animal to remain within this stable physiological state and the ramifications of leaving it. Romero et al. (Horm Behav 55(3):375–389, 2009) introduced the reactive scope model to provide a novel conceptual framework for the stress response that assumes an animal’s ability to tolerate a stressful stimulus may degrade over time in response to the stimulus. We provide a mathematical formulation for the reactive scope model using a system of ordinary differential equations and show that this model is capable of recreating existing experimental data. We also provide an experimental method that may be used to verify the model as well as several potential additions to the model. If future experimentation provides the necessary data to estimate the model’s parameters, the model presented here may be used to make quantitative predictions about physiological mediator levels during a stress response and predict the onset of homeostatic overload.
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- Award ID(s):
- 1655269
- PAR ID:
- 10450886
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Mathematical Biology
- Volume:
- 87
- Issue:
- 3
- ISSN:
- 0303-6812
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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