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Abstract

Systematists have long been fascinated by the astounding variation in species diversity across the various branches of the tree of life, a net result of the uneven rates at which lineages undergo speciation and extinction over time. The past 30 years have seen the development and widespread application of tools that allow diversification to be quantified and characterized in empirical data sets. These advances have, in turn, enabled the statistical evaluation of hypotheses about the causes behind the uneven distribution of species richness among lineages, leading to a more nuanced understanding of diversification rate variation, as reflected in an ever-expanding literature. Here, we provide a brief review of the current understanding of these models, the types of questions they address, and some of their collective limitations, with a focus on tree-based analyses of reconstructed phylogenies. Based on this overview, we outline future considerations in the lineage diversification research program, including the potential for machine learning to revolutionize the field by making model selection and parameter estimation more efficient in highly complex models. We interpret the recent slowdown in publication pace as a sign of a maturing field, where systematists are taking a step back after becoming better equipped to understand the technicalities and current limitations of these methods, leading to more careful applications and a greater embrace of uncertainty.

Document Type

Article

Publication Date

2026

Notes/Citation Information

© The Author(s) 2025. Published by Oxford University Press on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site-for further information please contact journals.permissions@oup.com

Digital Object Identifier (DOI)

https://doi.org/10.1093/sysbio/syaf086

Funding Information

This work was supported by National Science Foundation DEB 2323170 (R.Z.-F.). L.F.H.-D. was supported by the Chicago Fellows Program and National Science Foundation DEB 2325837.

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