Online Appendix and Cetacean Datasets for: The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology

Phylodynamic models generally aim at jointly inferring phylogenetic
relationships, model parameters, and more recently, the number of lineages
through time, 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 the past number of lineages. Here, we present
new methodological developments around the Fossilized Birth-Death Process
enabling us to (i) incorporate occurrence data in the likelihood function;
(ii) consider piecewise-constant birth, death and sampling rates; and
(iii) reconstruct the past number of lineages, 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 infer
the prevalence of the COVID-19 outbreak on the Diamond Princess ship, by
taking into account jointly the case count record (occurrences) along with
viral sequences for a fraction of infected individuals. In macroevolution,
we infer the diversity trajectory of cetaceans using molecular and
morphological data from extant taxa, morphological data from fossils, as
well as numerous fossil occurrences. The joint modeling of occurrences and
trees holds the promise to further bridge the gap between between
traditional epidemiology and pathogen genomics, as well as paleontology
and molecular phylogenetics.