Cryptic selection forces and dynamic heritability in generalized phenotypic evolution.

Area of Science:

• Evolutionary biology
• Quantitative genetics
• Population genetics

Background:

• Classical evolutionary models often focus on average phenotype fitness, overlooking complex population dynamics.
• Recent findings highlight multiple fitness peaks and frequency-dependent selection impacting evolutionary trajectories.
• Understanding phenotypic evolution requires methods that account for the full distribution of traits.

Purpose of the Study:

• To extend classical fitness gradient formulations to model phenotype distribution dynamics using moments (mean, variance, skewness).
• To develop a flexible, low-dimensional model adaptable to varying levels of biological detail.
• To provide a framework for studying phenotypic evolution in complex scenarios, including those where genetic analysis is difficult.

Main Methods:

• Developed a mathematical model describing phenotype distribution dynamics via its statistical moments.
• Adjusted the number of governing equations to control model complexity and detail.
• Compared model predictions against direct Wright-Fisher simulations across various fitness landscapes.

Main Results:

• The model successfully captures complex evolutionary dynamics not explained by classical theory.
• Identified cryptic selection forces arising from selection on trait ranges.
• Demonstrated the model's ability to describe time-varying heritability and nonlinear selection responses with asymmetric trait distributions.

Conclusions:

• The extended model offers a low-dimensional yet comprehensive description of phenotypic evolution.
• Provides a framework for understanding concepts like fitness gradients, selection pressures, and heritability in nuanced ways.
• Offers practical applications for studying evolution, especially when genetic data is limited.