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

• Published in: Journal of evolutionary biology Vol. 35; no. 4; pp. 520 - 538
• Main Authors: Martin, Jordan S., Jaeggi, Adrian V.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.04.2022; John Wiley and Sons Inc
• Subjects: Adaptation, Physiological - genetics; animal model; Animal models; Animals; assortment; Biological Evolution; Evolution; Genetic analysis; Mathematical models; Models, Animal; Norms; Parameters; Phenotype; Phenotypes; Phenotypic plasticity; Plastic properties; Plasticity; Programming languages; Quantitative genetics; reaction norm; Selection, Genetic; Social Behavior; social evolution; uncertainty; plasticity; reaction norm; assortment; animal model; social evolution
• ISSN: 1010-061X; 1420-9101; 1420-9101
• DOI: 10.1111/jeb.13900

Both assortment and plasticity can facilitate social evolution, as each may generate heritable associations between the phenotypes and fitness of individuals and their social partners. However, it currently remains difficult to empirically disentangle these distinct mechanisms in the wild, particularly for complex and environmentally responsive phenotypes subject to measurement error. To address this challenge, we extend the widely used animal model to facilitate unbiased estimation of plasticity, assortment and selection on social traits, for both phenotypic and quantitative genetic (QG) analysis. Our social animal models (SAMs) estimate key evolutionary parameters for the latent reaction norms underlying repeatable patterns of phenotypic interaction across social environments. As a consequence of this approach, SAMs avoid inferential biases caused by various forms of measurement error in the raw phenotypic associations between social partners. We conducted a simulation study to demonstrate the application of SAMs and investigate their performance for both phenotypic and QG analyses. With sufficient repeated measurements, we found desirably high power, low bias and low uncertainty across model parameters using modest sample and effect sizes, leading to robust predictions of selection and adaptation. Our results suggest that SAMs will readily enhance social evolutionary research on a variety of phenotypes in the wild. We provide detailed coding tutorials and worked examples for implementing SAMs in the Stan statistical programming language. Empirical study of the causes and evolutionary consequences of social interactions remains challenging. Raw associations between social partners' phenotypes are often biased by various forms of measurement error (left), and tend to confound distinct social effects within (plasticity) and between partners (assortment) over time (top right). By separating out these distinct mechanisms, social animal models facilitate more accurate predictions of social selection and adaptation (bottom right).