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

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 d..., , , [https://doi.org/10.5061/dryad.dfn2z35cg](https://doi.org/10.5061/dryad.dfn2z35cg)

# Description of the data and file structure

## **Data Description: Rawfiles_Jan1_2026.zip**

This dataset contains phylogenetic, trait, and environmental data used to investigate the evolution of woodiness in eudicot plants. The data were collected to examine the relationships between plant growth form (woody vs. herbaceous), vessel anatomy, plant height, climate variables, and leaf phenology across the eudicot clade. The data files and analytical outputs are organized in directories corresponding to specific figures in the manuscript (Figure_1_2_S1, Figure_3, Fig_4, Figure_5_S3_S4, Figure_S5, Figure_6). Each directory contains the input data files and output files required to generate the corresponding figure, allowing for reproducible analysis of each component of the study.

Missing values (NA): Across all data files and analytical outputs, NA denotes missing data arising from unavailable measuremen...,