A class of identifiable phylogenetic birth-death models

In a striking result, Louca and Pennell [S. Louca, M. W. Pennell, Nature 580,502-505 (2020)1 recently proved that a large class of phylogenetic birth-death models is statistically unidentifiable from lineage-through-time (LTT) data: Any pair of sufficiently smooth birth and death rate functions is congruent to an infinite collection of other rate functions, all of which have the same likelihood for any LTT vector of any dimension. As Louca and Pennell argue, this fact has distressing implications for the thousands of studies that have utilized birth-death models to study evolution. In this paper, we qualify their finding by proving that an alternative and widely used class of birth-death models is indeed identifiable. Specifically, we show that piecewise constant birth-death models can, in principle, be consistently estimated and distinguished from one another, given a sufficiently large extant timetree and some knowledge of the present-day population. Subject to mild regularity conditions, we further show that any unidentifiable birth-death model class can be arbitrarily closely approximated by a class of identifiable models. The sampling requirements needed for our results to hold are explicit and are expected to be satisfied in many contexts such as the phylodynamic analysis of a global pandemic.

Automated summary

Louca and Pennell recently demonstrated that a large class of phylogenetic birth-death models is statistically unidentifiable from lineage-through-time (LTT) data, while an alternative and widely used class of birth-death models is indeed identifiable. They further show that any unidentifiable birth-death model class can be arbitrarily closely approximated by a class of identifiable models, with specific sampling requirements that are expected to be met in many contexts such as the phylodynamic analysis of a global pandemic.