The Fossilized Birth–Death Model Is Identifiable

Time-dependent birth-death sampling models have been used in numerous studies to infer past evolutionary dynamics in different biological contexts, for example, speciation and extinction rates in macroevolutionary studies, or effective reproductive number in epidemiological studies. These models are branching processes where lineages can bifurcate, die, or be sampled with time-dependent birth, death, and sampling rates, generating phylogenetic trees. It has been shown that in some subclasses of such models, different sets of rates can result in the same distributions of reconstructed phylogenetic trees, and therefore, the rates become unidentifiable from the trees regardless of their size. Here, we show that widely used time-dependent fossilized birth-death (FBD) models are identifiable. This subclass of models makes more realistic assumptions about the fossilization process and certain infectious disease transmission processes than the unidentifiable birth-death sampling models. Namely, FBD models assume that sampled lineages stay in the process rather than being immediately removed upon sampling. The identifiability of the time-dependent FBD model justifies using statistical methods that implement this model to infer the underlying temporal diversification or epidemiological dynamics from phylogenetic trees or directly from molecular or other comparative data. We further show that the time-dependent FBD model with an extra parameter, the removal after sampling probability, is unidentifiable. This implies that in scenarios where we do not know how sampling affects lineages, we are unable to infer this extra parameter together with birth, death, and sampling rates solely from trees.

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Publication Details

Journal
Systematic Biology
Published
2024-10-17
DOI
https://doi.org/10.1093/sysbio/syae058
Citations
19
Primary Topic
Evolution and Paleontology Studies
Type
article
Field-Weighted Citation Impact
22.24
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article

The Fossilized Birth–Death Model Is Identifiable

Alexandra Gavryushkina, Alex Gavryushkin, Timothy G. Vaughan, Kate Truman
19 citations
Systematic Biology
Evolution and Paleontology Studies
22.24
article

The Fossilized Birth–Death Model Is Identifiable

Alexandra Gavryushkina, Alex Gavryushkin, Timothy G. Vaughan, Kate Truman
article en
19 citations

Abstract

Time-dependent birth-death sampling models have been used in numerous studies to infer past evolutionary dynamics in different biological contexts, for example, speciation and extinction rates in macroevolutionary studies, or effective reproductive number in epidemiological studies. These models are branching processes where lineages can bifurcate, die, or be sampled with time-dependent birth, death, and sampling rates, generating phylogenetic trees. It has been shown that in some subclasses of such models, different sets of rates can result in the same distributions of reconstructed phylogenetic trees, and therefore, the rates become unidentifiable from the trees regardless of their size. Here, we show that widely used time-dependent fossilized birth-death (FBD) models are identifiable. This subclass of models makes more realistic assumptions about the fossilization process and certain infectious disease transmission processes than the unidentifiable birth-death sampling models. Namely, FBD models assume that sampled lineages stay in the process rather than being immediately removed upon sampling. The identifiability of the time-dependent FBD model justifies using statistical methods that implement this model to infer the underlying temporal diversification or epidemiological dynamics from phylogenetic trees or directly from molecular or other comparative data. We further show that the time-dependent FBD model with an extra parameter, the removal after sampling probability, is unidentifiable. This implies that in scenarios where we do not know how sampling affects lineages, we are unable to infer this extra parameter together with birth, death, and sampling rates solely from trees.

Systematic BiologyVol. 74(1)
SIB Swiss Institute of Bioinformatics (CH), University of Canterbury (NZ), ETH Zurich (CH)
Good health and well-being
Openalex Percentile: Top 1%
Evolution and Paleontology Studies
22.24
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