Trait macroevolution in the presence of covariates

Statistical characterisations of traits evolving on phylogenies combine the contributions of unique and shared influences on those traits, potentially confusing the interpretation of historical events of macroevolution. The Fabric model, introduced in 2022, identifies historical events of directional shifts in traits (e.g. becoming larger/smaller, faster/slower over evolutionary time) and of changes in macroevolutionary 'evolvability' or the realised historical ability of a trait to explore its trait-space. Here we extend the model to accommodate situations in which the trait is correlated with one or more covarying traits. The Fabric-regression model identifies a unique component of variance in the trait that is free of influences from correlated traits, while simultaneously estimating directional and evolvability effects. We show in a dataset of 1504 Mammalian species that inferences about historical directional shifts in brain size and in its evolvability, having accounted for body size, differ qualitatively from inferences about brain size alone, including finding many new effects not visible in the whole trait. A class of fundamental macroevolutionary questions awaits testing on the variation uniquely attributable to traits, and the ability to accommodate statistically one or more covariates opens the possibility of bringing the formal methods of causal inference to phylogenetic-comparative studies.