Social animal models for quantifying plasticity, assortment, and selection on interacting phenotypes.

Area of Science:

• Evolutionary biology
• Quantitative genetics
• Behavioral ecology

Background:

• Assortment and plasticity influence social evolution by creating heritable associations between individuals and their social partners.
• Empirically distinguishing these mechanisms, especially for complex, environmentally responsive phenotypes, is challenging due to measurement error.

Purpose of the Study:

• To extend the animal model for unbiased estimation of plasticity, assortment, and selection on social traits.
• To introduce social animal models (SAMs) for analyzing phenotypic and quantitative genetic data.
• To address inferential biases caused by measurement error in social trait associations.

Main Methods:

• Developed social animal models (SAMs) to estimate evolutionary parameters for latent reaction norms.
• Utilized a simulation study to demonstrate SAM application and performance.
• Applied SAMs to both phenotypic and quantitative genetic analyses.

Main Results:

• SAMs provide unbiased estimation of plasticity, assortment, and selection on social traits.
• The models effectively handle measurement error in phenotypic associations.
• Simulations showed high power, low bias, and low uncertainty with sufficient repeated measurements and modest sample sizes.

Conclusions:

• SAMs offer a robust framework for studying social evolution in natural populations.
• The approach enhances predictions of selection and adaptation for various phenotypes.
• The study provides coding tutorials and examples for implementing SAMs in the Stan programming language.