Modelling trait heterogeneity and inferring causal links in the macroevolution of growth habit in eudicot angiosperms

Abstract

Phylogenetic comparative methods (PCMs) help researchers understand and predict trait evolutionary relationships. While improvements to PCMs have focused on increasing model complexity, understanding processes remains difficult due to persistent challenges in grounding complex models in biological reality and synthesizing findings across multiple analyses. We examined the evolution of growth habit in eudicots (75% of all angiosperms) and tested how variables such as vessel diameter, leaf phenology, and minimum temperature influence macroevolutionary inference. We used a series of PCMs to synthesize our understanding of trait interrelationships, explored plausible causal relationships using phylogenetic path analysis, and employed phylogenetic cross-validation to assess predictive performance among taxa. We found that discrete coding of growth form was linked to other measured and unmeasured traits, and that these interrelationships can help overcome limitations arising from incomplete data and simplistic coding of complex traits. Analysis of growth form using phylogenetic path analysis helps reconcile competing views of trait interrelationships from previous studies. Furthermore, including identified covariates improves prediction of growth habit and other traits. Our study shows that incorporating causal structure improves macroevolutionary inference, identifies when analyses that omit key causal traits become unreliable, and underscores the importance of integrating phylogenetic models with natural-history knowledge.