Evolutionary game theory and the adaptive dynamics approach: adaptation where individuals interact.

Area of Science:

• Evolutionary biology
• Theoretical ecology
• Population genetics

Background:

• Evolutionary game theory and adaptive dynamics are key to understanding gradual evolution and adaptation in interacting individuals.
• Modeling quantitative trait evolution in complex populations requires robust mathematical tools.

Purpose of the Study:

• To review basic tools for modeling quantitative trait evolution in complex populations.
• To bridge different models and interpret selection by collecting mathematical expressions for directional and disruptive selection.
• To explore how population structure influences the emergence of polymorphism.

Main Methods:

• Review and synthesis of mathematical expressions from evolutionary game theory and adaptive dynamics.
• Analysis of directional and disruptive selection in class- and group-structured populations.
• Examination of individual fitness in relation to trait expression and environmental context.

Main Results:

• Disruptive selection can lead to diversity via two main pathways: non-linear fitness increases with trait expression, or trait expression influencing context-specific fitness.
• Population structure is crucial for the emergence of unique polymorphic characteristics.
• Mathematical expressions for selection in structured populations are presented to facilitate model integration.

Conclusions:

• Population structure provides the foundation for the emergence of polymorphism.
• The presented framework aids in understanding the links between life history, social evolution, and evolutionary ecology.
• This work encourages further integration of fundamental branches within evolutionary biology.