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Introducing a general class of species diversification models for phylogenetic trees
Author(s) -
Richter Francisco,
Haegeman Bart,
Etienne Rampal S.,
Wit Ernst C.
Publication year - 2020
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12205
Subject(s) - phylogenetic tree , diversification (marketing strategy) , inference , extant taxon , maximum likelihood , biology , evolutionary biology , phylogenetics , computational phylogenetics , maximization , econometrics , computer science , mathematics , artificial intelligence , statistics , phylogenetic network , mathematical optimization , gene , genetics , business , marketing
Phylogenetic trees are types of networks that describe the temporal relationship between individuals, species, or other units that are subject to evolutionary diversification. Many phylogenetic trees are constructed from molecular data that is often only available for extant species, and hence they lack all or some of the branches that did not make it into the present. This feature makes inference on the diversification process challenging. For relatively simple diversification models, analytical or numerical methods to compute the likelihood exist, but these do not work for more realistic models in which the likelihood depends on properties of the missing lineages. In this article, we study a general class of species diversification models, and we provide an expectation‐maximization framework in combination with a uniform sampling scheme to perform maximum likelihood estimation of the parameters of the diversification process.