71: New Bayesian Models for the Heterogeneous Evolution of Count Variables

Music City Center 

Evolution proceeds at varying rates across the tree of life. Researchers have developed models to reconstruct how the evolution of traits, such as animal body size, might have sped up or slowed down along the branches of the tree of life. However, these models were created for continuous variables, thereby inappropriate for discrete traits. Here, we present new Bayesian models for characterizing the evolutionary rate dynamics of count variables. We work under the framework of a phylogenetic tree, a tree-like network comprising nodes (representing present-day or extinct species) and edges with lengths typically representing time. We develop five stochastic processes that differ in distributions (e.g., triangle, Poisson, etc.) that govern how a count trait value changes from the tree root to terminal nodes. Trait values are expected to deviate more from the common ancestor with elapsed time, but traits could change faster or slower than predicted by just time. To model these rate shifts, we add a parameter that allows more or less deviations going from an ancestor to a descendant node. As an empirical case study, we apply our new methods to the evolution of animal chromosome count.

evolution

phylogenetic

stochastic

Bayesian 

Section on Statistics and the Environment