Aguirre-Fernandez, G. | bioRxiv Science

bioRxiv2020

A skyline birth-death process for inferring the population size from a reconstructed tree with occurrences

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, population size through time for clades of interest, based on molecular sequence data. In the fields of epidemiology and macroevolution these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, i.e. lacking morphological and molecular data, which we call occurrences, provide invaluable information to reconstruct past population sizes. Here, we present new methodological developments around the Fossilized Birth-Death Process enabling us to (i) efficiently incorporate occurrence data while remaining computationally tractable and scalable; (ii) consider piecewise-constant birth, death and sampling rates; and (iii) reconstruct past population sizes, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical datasets stemming from the fields of epidemiology and macroevolution. In epidemiology, we apply our model to the Covid-19 outbreak on the Diamond Princess ship. We infer the total prevalence throughout the outbreak, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we present an empirical case study of cetaceans. We infer the diversity trajectory using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. Our case studies highlight that the advances we present allow us to further bridge the gap between between epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics.

evolutionary biology↗

bioRxiv2018

Ignoring stratigraphic age uncertainty leads to erroneous estimates of species divergence times under the fossilized birth-death process

Fossil information is essential for estimating species divergence times, and can be integrated into Bayesian phylogenetic inference using the fossilized birth-death (FBD) process. An important aspect of palaeontological data is the uncertainty surrounding specimen ages, which can be handled in different ways during inference. The most common approach is to fix fossil ages to a point estimate within the known age interval. Alternatively, age uncertainty can be incorporated by using priors, and fossil ages are then directly sampled as part of the inference. This study presents a comparison of alternative approaches for handling fossil age uncertainty in analysis using the FBD process. Based on simulations, we find that fixing fossil ages to the midpoint or a random point drawn from within the stratigraphic age range leads to biases in divergence time estimates, while sampling fossil ages leads to estimates that are similar to inferences that employ the correct ages of fossils. Second, we show a comparison using an empirical dataset of extant and fossil cetaceans, which confirms that different methods of handling fossil age uncertainty lead to large differences in estimated node ages. Stratigraphic age uncertainty should thus not be ignored in divergence time estimation and instead should be incorporated explicitly.

paleontology↗