ArticleProceedings of the National Academy of Sciences of the United States of America2022
A class of identifiable phylogenetic birth-death models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Unidentifiability and false-positive inference in state-dependent diversification models.Nature communications · 2026Article
- Exploring the impact of Cenozoic climate change on diversification of the Australian endemic Eurepini crickets (Orthoptera: Gryllidae: Eneopterinae).Communications biology · 2026Article
- Exact and efficient phylodynamic simulation from arbitrarily large populations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The effects of cryptic diversity on diversification dynamics analyses in Crocodylia.Proceedings. Biological sciences · 2025Article
- Reading tree leaves: inferring speciation anfd extinction processes using phylogenies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- The Fossilized Birth-Death Model Is Identifiable.Systematic biology · 2025Article
- Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies.Bioinformatics and biology insights · 2025Article
- Commonly used Bayesian diversification methods lead to biologically meaningful differences in branch-specific rates on empirical phylogenies.Evolution letters · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.PLoS computational biology · 2024Article
- Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction.Cambridge prisms. Extinction · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.bioRxiv : the preprint server for biology · 2023Article
- Alternate histories in macroevolution.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Rapidly changing speciation and extinction rates can be inferred in spite of nonidentifiability.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Abstract
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.
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