Predator-Prey Evolution from an Eco-evolutionary Trade-off Model: The Role of Trait Differentiation

Area of Science:

• Ecology
• Evolutionary Biology
• Mathematical Modeling

Background:

• Eco-evolutionary dynamics are complex, involving interactions between ecological processes and evolutionary changes.
• Existing models often struggle to capture the interplay between population dynamics and trait evolution efficiently.
• Trait distributions, particularly in predator-prey systems, are crucial for understanding ecosystem stability.

Purpose of the Study:

• To develop and validate a novel eco-evolutionary modelling framework for simulating trait distribution evolution.
• To compare the efficacy of a new population-level model with a traditional phenotype-level model.
• To investigate the role of stabilizing selection in predator-prey systems using this framework.

Main Methods:

• Developed a novel eco-evolutionary modelling framework using canonical equations for population size, average trait value, and trait differentiation (Q).
• Modeled trait distributions using beta distributions, with Q as a phenotype analogue of Wright's fixation index.
• Compared simulation results from the population model with a phenotype model under stabilizing selection in a predator-prey system with trade-offs.

Main Results:

• The population model significantly reduces the number of equations needed to simulate eco-evolutionary systems (by orders of magnitude) without losing accuracy.
• Both the population and phenotype models showed close agreement, predicting that evolution stabilizes initially oscillatory predator-prey systems under stabilizing selection.
• The population model provided insights into system properties not apparent from the phenotype model.

Conclusions:

• The novel eco-evolutionary modelling framework is effective for simulating trait distribution evolution in predator-prey systems.
• This population-level approach offers a computationally efficient and insightful alternative to traditional phenotype-level models.
• The framework's ability to capture stabilizing selection dynamics highlights its potential applicability across various ecosystems.