A class of identifiable phylogenetic birth–death models

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 35; pp. 1 - 10
• Main Authors: Legried, Brandon, Terhorst, Jonathan
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 30.08.2022
• Subjects: Biological Evolution; Biological Sciences; Birth; Death; Humans; Markov Chains; Models, Biological; Mortality; Pandemics; Parturition; Phylogenetics; Phylogeny; Population Dynamics; Statistical analysis; birth–death models; phylogenetics; phylodynamics; identifiability
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2119513119

In a striking result, Louca and Pennell [S. Louca, M.W. Pennell, Nature 580, 502–505 (2020)] 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.