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Learning to hunt: A data-driven stochastic feedback control model of predator-prey interactions
Deze Liu1, Mohammad Tuqan1, Daniel Burbano1
1Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, 08854, NJ, USA.
Journal of Theoretical Biology
|December 11, 2024
Summary
This study models dolphin hunting behavior using adaptive learning strategies. Findings show that noise levels in predator-prey dynamics can influence fish survival or dolphin hunting success.
Area of Science:
- Behavioral Ecology
- Mathematical Biology
- Neuroscience
Background:
- Predator-prey dynamics are crucial for species survival and evolution.
- Sensory-motor control strategies are key to hunting and evasion.
- Analytical models face challenges due to adaptive and stochastic animal behavior.
Purpose of the Study:
- To develop a data-driven mathematical model of dolphin hunting behavior.
- To understand adaptive learning strategies in predator-prey interactions.
- To explore the impact of stochasticity on hunting success.
Main Methods:
- Utilized feedback control systems and stochastic differential equations.
- Developed a mathematical model based on empirical data.
- Validated the model through numerical experiments.
Main Results:
- The model captures adaptive learning in dolphins hunting fish.
- Numerical experiments confirmed the model's efficacy.
- Varying noise levels were shown to affect survival or predation outcomes.
Conclusions:
- The model provides insights into adaptive learning strategies in predator-prey interactions.
- Stochasticity plays a significant role in determining the success of predator-prey encounters.
- This framework can be applied to study other animal behaviors.
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