Evaluating the Robustness of Process-Pattern Links Using Phylogenies — Insights from a Comparison of Eight Simulation Models
Abstract
Macroevolutionary studies have shown that the shape of phylogenetic trees differs in space, time, and between taxa. It is commonly assumed that these differences in tree shape reflect variability in the underlying ecological and evolutionary processes that produced them, and mechanistic eco-evolutionary models are increasingly used to explore this link. A concern in this context is whether conclusions drawn from such mechanistic models are robust to idiosyncrasies in how eco-evolutionary processes are formalized in the models. Here, we use eight mechanistic macroevolutionary models to study how 52 metrics of phylogenetic tree shape respond to variation in the strength of five fundamental processes: competition, dispersal, environmental filtering, niche conservatism, and speciation. We find that models agree on how some tree metrics respond to changes in these processes, in particular dispersal and speciation. However, no tree metric uniquely correlated with a single process, suggesting that single tree metrics have limited utility as shortcuts for inferring the underlying eco-evolutionary processes. Moreover, although it was possible to infer the underlying processes if the data-generating model was known, inference was not consistent across the different models. We conclude that the relationship between phylogenetic patterns and eco-evolutionary processes in macroevolutionary analysis is likely sensitive to the structural and mechanistic details of how a given process is implemented within models.
What the paper shows and why it matters (AI-generated)
Ecologists routinely read phylogenetic tree shape as a fingerprint of the evolutionary process that produced it — high diversification here, strong competition there. Running eight different simulation models side by side against the same 52 tree-shape metrics shows that fingerprint is much less reliable than assumed: no single metric tracked one process uniquely, and even knowing the right process, inference varied across models built to represent it. A caution against reading too much mechanism into tree shape alone, from a genuinely multi-model test rather than a single model's self-assessment.