Raup, D. M. Biological extinction in Earth history. Science 231, 1528–1533 (1986).
Google Scholar
Benton, M. J. The Red Queen and the Court Jester: species diversity and the role of biotic and abiotic factors through time. Science 323, 728–732 (2009).
Google Scholar
Harmon, L. J. & Harrison, S. Species diversity is dynamic and unbounded at local and continental scales. Am. Nat. 185, 584–593 (2015).
Google Scholar
Rabosky, D. L. & Hurlbert, A. H. Species richness at continental scales is dominated by ecological limits. Am. Nat. 185, 572–583 (2015).
Google Scholar
Gavrilets, S. & Losos, J. B. Adaptive radiation: contrasting theory with data. Science 323, 732–737 (2009).
Google Scholar
Gould, S. J., Gilinsky, N. L. & German, R. Z. Asymmetry of lineages and the direction of evolutionary time. Science 236, 1437–1441 (1987).
Google Scholar
Žliobaitė, I., Fortelius, M. & Stenseth, N. C. Reconciling taxon senescence with the Red Queen’s hypothesis. Nature 552, 92–95 (2017).
Google Scholar
Morlon, H., Parsons, T. L. & Plotkin, J. B. Reconciling molecular phylogenies with the fossil record. Proc. Natl Acad. Sci. USA 108, 16327–16332 (2011).
Google Scholar
Quental, T. B. & Marshall, C. R. How the Red Queen drives terrestrial mammals to extinction. Science 341, 290–292 (2013).
Google Scholar
Alfaro, M. E. et al. Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. Proc. Natl Acad. Sci. USA 106, 13410–13414 (2009).
Google Scholar
Stadler, T. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Natl Acad. Sci. USA 108, 6187–6192 (2011).
Google Scholar
Etienne, R. S. & Haegeman, B. A conceptual and statistical framework for adaptive radiations with a key role for diversity dependence. Am. Nat. 180, E75–E89 (2012).
Google Scholar
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9, e89543 (2014).
Google Scholar
Heath, T. A., Huelsenbeck, J. P. & Stadler, T. The fossilized birth–death process for coherent calibration of divergence-time estimates. Proc. Natl Acad. Sci. USA 111, E2957–E2966 (2014).
Google Scholar
Gavryushkina, A. et al. Bayesian total-evidence dating reveals the recent crown radiation of penguins. Syst. Biol. 66, 57–73 (2017).
Google Scholar
Quintero, I., Lartillot, N. & Morlon, H. Imbalanced speciation pulses sustain the radiation of mammals. Science 384, 1007–1012 (2024).
Google Scholar
Hauffe, T., Cantalapiedra, J. L. & Silvestro, D. Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks. Sci. Adv. 10, eadl2643 (2024).
Google Scholar
Burin, G., Alencar, L. R., Chang, J., Alfaro, M. E. & Quental, T. B. How well can we estimate diversity dynamics for clades in diversity decline? Syst. Biol. 68, 47–62 (2019).
Google Scholar
Silvestro, D., Warnock, R. C., Gavryushkina, A. & Stadler, T. Closing the gap between palaeontological and neontological speciation and extinction rate estimates. Nat. Commun. 9, 5237 (2018).
Google Scholar
Warnock, R. C., Heath, T. A. & Stadler, T. Assessing the impact of incomplete species sampling on estimates of speciation and extinction rates. Paleobiology 46, 137–157 (2020).
Google Scholar
Billaud, O., Moen, D., Parsons, T. L. & Morlon, H. Estimating diversity through time using molecular phylogenies: old and species-poor frog families are the remnants of a diverse past. Syst. Biol. 69, 363–383 (2020).
Google Scholar
Rabosky, D. L. Ecological limits and diversification rate: alternative paradigms to explain the variation in species richness among clades and regions. Ecol. Lett. 12, 735–743 (2009).
Google Scholar
Sepkoski, J. J. Ten years in the library: new data confirm paleontological patterns. Paleobiology 19, 43–51 (1993).
Google Scholar
Alroy, J. Dynamics of origination and extinction in the marine fossil record. Proc. Natl Acad. Sci. USA 105, 11536–11542 (2008).
Google Scholar
Foote, M. Symmetric waxing and waning of marine invertebrate genera. Paleobiology 33, 517–529 (2007).
Google Scholar
Morlon, H., Potts, M. D. & Plotkin, J. B. Inferring the dynamics of diversification: a coalescent approach. PLoS Biol. 8, e1000493 (2010).
Google Scholar
Hohmann, N. & Jarochowska, E. Enforced symmetry: the necessity of symmetric waxing and waning. PeerJ 7, e8011 (2019).
Google Scholar
Nee, S. Birth–death models in macroevolution. Annu. Rev. Ecol. Evol. Syst. 37, 1–17 (2006).
Google Scholar
Simpson, G. G.Tempo and Mode in Evolution (Columbia Univ. Press, 1953).
Schluter, D.The Ecology of Adaptive Radiations (Oxford Univ. Press, 2000).
Calderón del Cid, C. et al. The clade replacement theory: a framework to study age-dependent extinction. J. Evol. Biol. 37, 290–301 (2024).
Google Scholar
Hughes, M., Gerber, S. & Wills, M. A. Clades reach highest morphological disparity early in their evolution. Proc. Natl Acad. Sci. USA 110, 13875–13879 (2013).
Google Scholar
Raup, D. M. & Sepkoski, J. J. Mass extinctions in the marine fossil record. Science 215, 1501–1503 (1982).
Google Scholar
Bambach, R. K., Knoll, A. H. & Wang, S. C. Origination, extinction, and mass depletions of marine diversity. Paleobiology 30, 522–542 (2004).
Google Scholar
Stadler, T. Sampling-through-time in birth–death trees. J. Theor. Biol. 267, 396–404 (2010).
Google Scholar
Truman, K., Vaughan, T. G., Gavryushkin, A. & Gavryushkina, A. S. The fossilised birth–death model is identifiable. Syst. Biol. 74, 112–123 (2025).
Jablonski, D. Heritability at the species level: analysis of geographic ranges of cretaceous mollusks. Science 238, 360–363 (1987).
Google Scholar
Tanner, M. A. & Wong, W. H. The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. 82, 528–540 (1987).
Google Scholar
Höhna, S. et al. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Syst. Biol. 65, 726–736 (2016).
Google Scholar
Maliet, O. & Morlon, H. Fast and accurate estimation of species-specific diversification rates using data augmentation. Syst. Biol. 71, 353–366 (2022).
Google Scholar
Holland, S. M. The non-uniformity of fossil preservation. Phil. Trans. R. Soc. B 371, 20150130 (2016).
Google Scholar
Pett, W. & Heath, T. A. in Phylogenetics in the Genomic Era (eds Scornavacca, C. et al.) 5.1:1–5.1:18 (2020); https://hal.science/hal-02536361
Andréoletti, J. et al. The occurrence birth–death process for combined-evidence analysis in macroevolution and epidemiology. Syst. Biol. 71, 1440–1452 (2022).
Google Scholar
Cooper, R. B., Flannery-Sutherland, J. T. & Silvestro, D. DeepDive: estimating global biodiversity patterns through time using deep learning. Nat. Commun. 15, 4199 (2024).
Google Scholar
Foote, M. Diversity-dependent diversification in the history of marine animals. Am. Nat. 201, 680–693 (2023).
Google Scholar
Barnes, B. D., Sclafani, J. A. & Zaffos, A. Dead clades walking are a pervasive macroevolutionary pattern. Proc. Natl Acad. Sci. USA 118, e2019208118 (2021).
Google Scholar
Gould, S. J., Raup, D. M., Sepkoski, J. J., Schopf, T. J. & Simberloff, D. S. The shape of evolution: a comparison of real and random clades. Paleobiology 3, 23–40 (1977).
Google Scholar
Maliet, O., Hartig, F. & Morlon, H. A model with many small shifts for estimating species-specific diversification rates. Nat. Ecol. Evol. 3, 1086–1092 (2019).
Google Scholar
Van Valen, L. The Red Queen. Am. Nat. 111, 809–810 (1977).
Google Scholar
Eldredge, N. & Gould, S. J. in Models in Paleobiology (ed. Schopf, T. J. M.) 82–115 (Freeman, Cooper & Co., 1972).
Hunt, G. The relative importance of directional change, random walks, and stasis in the evolution of fossil lineages. Proc. Natl Acad. Sci. USA 104, 18404–18408 (2007).
Google Scholar
Sanisidro, O., Mihlbachler, M. C. & Cantalapiedra, J. L. A macroevolutionary pathway to megaherbivory. Science 380, 616–618 (2023).
Google Scholar
Van Valen, L. A new evolutionary law. Evol. Theory 1, 1–30 (1973).
Spiridonov, A. & Lovejoy, S. Life rather than climate influences diversity at scales greater than 40 million years. Nature 607, 307–312 (2022).
Google Scholar
Pearson, P. N. Investigating age dependency of species extinction rates using dynamic survivorship analysis. Hist. Biol. 10, 119–136 (1995).
Google Scholar
Nietzsche, F. Thus Spoke Zarathustra: A Book for All and None (Random House, 1995).
Fischhoff, B. Hindsight is not equal to foresight: the effect of outcome knowledge on judgment under uncertainty. J. Exp. Psychol. 1, 288–299 (1975).
Kidwell, S. M. & Holland, S. M. The quality of the fossil record: implications for evolutionary analyses. Annu. Rev. Ecol. Syst. 33, 561–588 (2002).
Google Scholar
Silvestro, D., Salamin, N. & Schnitzler, J. PyRate: a new program to estimate speciation and extinction rates from incomplete fossil data. Methods Ecol. Evol. 5, 1126–1131 (2014).
Google Scholar
Maddison, W. P., Midford, P. E. & Otto, S. P. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).
Google Scholar
Mitchell, J. S., Etienne, R. S. & Rabosky, D. L. Inferring diversification rate variation from phylogenies with fossils. Syst. Biol. 68, 1–18 (2019).
Google Scholar
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953).
Google Scholar
Hastings, W. K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109 (1970).
Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. Julia: a fresh approach to numerical computing. SIAM Rev. 59, 65–98 (2017).
Google Scholar
Huelsenbeck, J. P., Rannala, B. & Masly, J. P. Accommodating phylogenetic uncertainty in evolutionary studies. Science 288, 2349–2350 (2000).
Google Scholar
Stadler, T., Gavryushkina, A., Warnock, R. C. M., Drummond, A. J. & Heath, T. A. The fossilized birth–death model for the analysis of stratigraphic range data under different speciation modes. J. Theor. Biol. 447, 41–55 (2018).
Google Scholar
Stolz, U., Gavryushkina, A., Vaughan, T. G., Stadler, T. & Allen, B. J. Enhancing evolutionary timelines: the impact of stratigraphic range information on phylogenetic inference. Preprint at bioRxiv https://doi.org/10.1101/2025.04.17.649084 (2025).
Varela, S., González Hernández, J. & Fabris Sgarbi, L. paleobioDB: download and process data from the paleobiology database. R package v.0.7.0. CRAN https://CRAN.R-project.org/package=paleobioDB (2020).
Zaffos, A. A. velociraptr: Fossil Analysis. R package v.1.1.0. CRAN https://CRAN.R-project.org/package=velociraptr (2019).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2023).
Quintero, I., Landis, M. J., Jetz, W. & Morlon, H. The build-up of the present-day tropical diversity of tetrapods. Proc. Natl Acad. Sci. USA 120, e2220672120 (2023).
Google Scholar
Stan Reference Manual: Version 2.36.0 (Stan Development Team, 2024).
Gabry, J., Češnovar, R., Johnson, A. & Bronder, S. CmdStanR: R interface to ’CmdStan’. R package v.0.9.0. Stan https://mc-stan.org/cmdstanr/ (2025).
Rabosky, D. L. Diversity-dependence, ecological speciation, and the role of competition in macroevolution. Annu. Rev. Ecol. Evol. Syst. 44, 481–502 (2013).
Google Scholar
Etienne, R. S. et al. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. R. Soc. B 279, 1300–1309 (2012).
Google Scholar
Quintero, I. Supplementary dataset for “The rise, decline and fall of clades”. Zenodo https://doi.org/10.5281/zenodo.15535408 (2025).
