A NEW MODELING FRAMEWORK FOR FITTING AND EVALUATING UNIVARIATE AND MULTIVARIATE MODELS OF PHYLETIC EVOLUTION

Ancestor–descendant series of populations from the fossil record (evolutionary sequences) represent observations of phenotypic evolution on time intervals in-between micro- and macroevolutionary time scales. Such data are thus key in reconciling observations of evolution across a handful of generations with evolutionary changes observed across millions of years. Analyses of evolutionary sequences using Gene Hunt’s paleoTS framework have for more than 15 years generated new knowledge on phenotypic evolution at the intersection between paleontology and evolutionary biology. Here, I present evoTS, an R package that builds on – and extends – paleoTS. The combined suite of univariate models in paleoTS and evoTS allow for more detailed and nuanced investigations of evolutionary trait dynamics in fossil lineages. For example, some of the univariate models implemented in evoTS allow for a changing adaptive landscape, making it possible to test whether a fixed or dynamic adaptive landscape best fit a given evolutionary sequence. The multivariate models in evoTS allow for sophisticated investigations of drivers of evolution, including assessing evidence for adaptation, constraint and correlated evolution in multivariate trait data. I reanalyze already published univariate and multivariate evolutionary sequences from the fossil record and show how the models and features in evoTS can enrich how we interpret and understand phenotypic change within lineages.