Modeling 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 traitevolutionary relationships. While improvements to PCMs have focused on increasing modelcomplexity, 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) andtested how variables such as vessel diameter, leaf phenology, and minimum temperatureinfluence macroevolutionary inference. We used a series of PCMs to synthesize our understanding of trait interrelationships, explored plausible causal relationships using phylogeneticpath analysis, and employed phylogenetic cross-validation to assess predictive performanceamong 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 fromincomplete data and simplistic coding of complex traits. Analysis of growth form using phylo-genetic path analysis helps reconcile competing views of trait interrelationships from previousstudies. Furthermore, including identified covariates improves prediction of growth habit andother traits. Our study shows that incorporating causal structure improves macroevolutionary inference,identifies when analyses that omit key causal traits become unreliable, and underscores theimportance of integrating phylogenetic models with natural-history knowledge