Understanding processes of phenotypic evolution is one of the fundamental challenges in evolutionary biology. However, available comparative phylogenetic methods are only capable to model evolutionary dynamics in trait-by-trait manner (i.e. all traits are treated separately). At the same time, every phenotype exhibits a complex network of relationships between organismal traits. These relationships along with the properties of the traits represent the anatomy ontology whose structure has been ignored in modeling trait evolution. The present arsenal of statistical phylogenetics lacks any method capable to model evolution of entire phenotype conditional on relationships among organismal traits i.e. anatomy ontology.
In this project, I use the statistical framework of Bayesian networks to develop new models which allow reconstruction of evolutionary dynamics in entire phenotype. The inference tools for such models will be implemented in a separate R package.
Such ontology-informed models of evolution will allow addressing major biological questions on (1) the adaptive dynamics of morphology over space and time, (2) the evolutionary dynamics of morphological complexity over space and time, and (3) the origin of evolutionary novelties. Additionally, they have a potential to enhance development of methods for mapping phenotypic and genotypic traits.