Jordan Scott Martin 

Estimating quantitative genetic and phenotypic (co)variances is crucial for investigating evolutionary ecological phenomena such as developmental integration, life history tradeoffs, and niche specialization, as well as for describing selection and predicting multivariate evolution in the wild. While most studies assume (co)variances are fixed over short timescales, environmental heterogeneity can rapidly modify the variation of and associations among organisms’ traits. Here I synthesize prior random regression and double hierarchical animal models to develop a novel covariance reaction norm (CRN) model for detecting how trait (co)variances respond to complex (i.e., continuous, multivariate, and potentially nonlinear) environmental change, even in the absence of repeated individual measurements or experimental breeding designs. After introducing the CRN model, I validate its implementation in Stan, demonstrating unbiased Bayesian inference. I then apply the model to long-term field data on cooperation among meerkats (Suricata suricatta). I find nonlinear effects of group size on the genetic (co)variances of cooperative behaviors, leading to increased social niche specialization among foraging and pup feeding versus babysitting tasks in larger groups. Multivariate gene-by-environment interactions are also observed in response to age, sex, and dominance status. R code and a tutorial are provided to aid empiricists in applying CRN models to their own datasets.