Studying speciation and extinction dynamics from phylogenies: addressing identifiability issues

Hélène Morlon, Stéphane Robin, Florian Hartig

Trends in Ecology & Evolution, 37(6), 497–506 (2022)
Cite this
@article{morlon2022studying,
  author = {Hélène Morlon and Stéphane Robin and Florian Hartig},
  title = {Studying speciation and extinction dynamics from phylogenies: addressing identifiability issues},
  journal = {Trends in Ecology & Evolution},
  volume = {37},
  number = {6},
  pages = {497–506},
  year = {2022},
  doi = {10.1016/j.tree.2022.02.004},
}

DOI: 10.1016/j.tree.2022.02.004
Cited by 88 (Google Scholar) · 87 (OpenAlex), as of 07 September 2026

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Abstract

A lot of what we know about past speciation and extinction dynamics is based on statistically fitting birth-death processes to phylogenies of extant species. Despite their wide use, the reliability of these tools is regularly questioned. It was recently demonstrated that vast “congruent” sets of alternative diversification histories cannot be distinguished (i.e., are not identifiable) using extant phylogenies alone, reanimating the debate about the limits of phylogenetic diversification analysis. Here, we summarize what we know about the identifiability of the birth-death process and how identifiability issues can be addressed. We conclude that extant phylogenies, when combined with appropriate prior hypotheses and regularization techniques, can still tell us a lot about past diversification dynamics.

What the paper shows and why it matters (AI-generated)

A landmark 2020 critique showed that wildly different speciation-extinction histories can produce statistically indistinguishable phylogenies — an identifiability problem serious enough to threaten decades of diversification research built on fitting birth-death models to trees of living species. This review takes stock of that critique and its aftermath and concludes the field isn't as sunk as it first appeared: combined with reasonable prior assumptions and regularization, extant phylogenies can still recover real information about the past. Citing work since, from deep-learning parameter estimators to new fossil-integrated models, suggests the field responded by building better tools rather than abandoning the enterprise.