A framework to study and predict functional trait syndromes using phylogenetic and environmental data

Traits do not evolve in isolation but often as part of integrated trait syndromes, yet the relative contributions of environmental effects and evolutionary history on traits and their correlations are not easily resolved. In the present study, we develop a methodological framework to elucidate eco-evolutionary patterns in functional trait syndromes. We do so by separating the amount of variance and covariance related to phylogenetic heritage and environmental variables (environmental phylogenetic conservatism), only phylogenetic heritage (non-attributed phylogenetic conservatism) and only to environmental variables (evolutionarily labile environmental effects). Variance-covariance structures of trait syndromes are displayed as networks. We then use this framework to guide a newly derived imputation method based on machine learning models that predict trait values for unsampled taxa, considering environmental and phylogenetic information as well as trait covariation. TrEvol is presented as an R package providing a unified set of methodologies to study and predict multivariate trait patterns and improve our capacity to impute trait values. To illustrate its use, we leverage both simulated data and species-level traits related to hydraulics and the leaf economics spectrum, in relation to an aridity index, demonstrating that most trait correlations can be attributed to environmental phylogenetic conservatism. This conceptual framework can be employed to examine issues ranging from the evolution of trait adaptation at different phylogenetic depths to intraspecific trait variation.