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

Abstract

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.

Behavioural ecology at the spatial–social interface

“…The relationship between social phenotypes and the spatial environment fits well within the existing theoretical and methodological frameworks used to test the social reaction norm hypothesis (Table 3; Martin & Jaeggi, 2021; Strickland et al ., 2021). Examining how social phenotypes change as a function of environmental features (e.g.…”

A variance partitioning perspective of assortative mating: Proximate mechanisms and evolutionary implications

“…One may decide to separately investigate the presence of indirect genetic effects using statistical approaches such as trait‐based or covariance partitioning models (Dingemanse & Araya‐Ajoy, 2015), but these approaches may also be biased in the presence of pair‐level or genetic‐level correlations between partners’ traits. In this special issue, Martin and Jaeggi (2021) propose a new statistical method (the Social Animal Model) to partition such correlations from indirect genetic effects. This trait‐based errors‐in‐variables model allows estimating individuals’ responses to one or multiple partner's phenotypes while accounting for social environments, social feedbacks and other environmental effects.…”

Covariance reaction norms: A flexible approach to estimating continuous environmental effects on quantitative genetic and phenotypic (co)variances

“…Extensive prior work has provided detailed overview of the animal model and its various extensions (e.g. Nussey et al, 2007;Wilson et al, 2010;Thomson et al, 2018;Martin & Jaeggi, 2022). Therefore, I focus herein on a highly simplified presentation of the animal model to highlight novel extensions, as well as to avoid detailed discussion of general issues in regression analysis such as the inclusion of various kinds of fixed and random effects With the G matrix being scaled using the Kronecker product ⊗ by a relatedness matrix A that quantifies pairwise relatedness among subjects, calculated using standard pedigree methods or molecular approaches.…”