Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Fossil data support a pre-Cretaceous origin of flowering plants

Nature Ecology & Evolution volume 5, pages 449–457 (2021)Cite this article

Subjects

Abstract

Flowering plants (angiosperms) are the most diverse of all land plants, becoming abundant in the Cretaceous and achieving dominance in the Cenozoic. However, the exact timing of their origin remains a controversial topic, with molecular clocks generally placing their origin much further back in time than the oldest unequivocal fossils. To resolve this discrepancy, we developed a Bayesian method to estimate the ages of angiosperm families on the basis of the fossil record (a newly compiled dataset of ~15,000 occurrences in 198 families) and their living diversity. Our results indicate that several families originated in the Jurassic, strongly rejecting a Cretaceous origin for the group. We report a marked increase in lineage accumulation from 125 to 72 million years ago, supporting Darwin’s hypothesis of a rapid Cretaceous angiosperm diversification. Our results demonstrate that a pre-Cretaceous origin of angiosperms is supported not only by molecular clock approaches but also by analyses of the fossil record that explicitly correct for incomplete sampling.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Examples of diversity trajectories simulated using a conditioned Brownian bridge.
Fig. 2: Performance of the BBB method assessed through 200 simulations with randomly varying sampling rates through time.
Fig. 3: Estimated times of origin of angiosperm families and cumulative family diversity plot.
Fig. 4: Family-level origination rates inferred from the estimated diversity trajectories of the sampled families (Fig. 3b).
Fig. 5: Comparison between our estimates of the age of origin of angiosperm families and estimates based on a molecular clock and the stratigraphic confidence interval.

Similar content being viewed by others

Data availability

All data analysed in this study are available in Supplementary Table 3 and in a permanent Zenodo (zenodo.org) repository with doi: 10.5281/zenodo.4290423.

Code availability

We implemented the BBB method in Python v.3. The code and input files are available in Supplementary Table 3 and in a permanent Zenodo (zenodo.org) repository with doi: 10.5281/zenodo.4290423. The code and input files and any future updates of the program are additionally available as an open access repository: https://github.com/dsilvestro/rootBBB.

References

  1. Clarke, J. T., Warnock, R. C. M. & Donoghue, P. C. J. Establishing a time-scale for plant evolution. New Phytol. 192, 266–301 (2011).

    Article  PubMed  Google Scholar 

  2. Friis, E. M., Pedersen, K. R. & Crane, P. R. Diversity in obscurity: fossil flowers and the early history of angiosperms. Phil. Trans. R. Soc. B 365, 369–382 (2010).

    Article  PubMed  Google Scholar 

  3. Coiro, M., Doyle, J. A. & Hilton, J. How deep is the conflict between molecular and fossil evidence on the age of angiosperms? New Phytol. 223, 83–99 (2019).

    Article  PubMed  Google Scholar 

  4. Donoghue, P. Evolution: the flowering of land plant evolution. Curr. Biol. 29, R738–R761 (2019).

    Article  Google Scholar 

  5. Buggs, R. J. A. The deepening of Darwin’s abominable mystery. Nat. Ecol. Evol. 1, 0169 (2017).

    Article  Google Scholar 

  6. Herendeen, P. S., Friis, E. M., Pedersen, K. R. & Crane, P. R. Palaeobotanical redux: revisiting the age of the angiosperms. Nat. Plants 3, 17015 (2017).

    Article  PubMed  Google Scholar 

  7. Darwin, C. More Letters of Charles Darwin Vol. 2 (John Murray, 1903).

  8. Friedman, W. E. The meaning of Darwin’s ‘abominable mystery’.Am. J. Bot. 96, 5–21 (2009).

    Article  PubMed  Google Scholar 

  9. Marshall, C. R. Five paleobiological laws needed to understand the evolution of the living biota. Nat. Ecol. Evol. 1, 0165 (2017).

    Article  Google Scholar 

  10. Slater, G. & Harmon, L. J. Unifying fossils and phylogenies for comparative analyses of diversification and trait evolution. Methods Ecol. Evol. 4, 699–702 (2013).

    Article  Google Scholar 

  11. Didier, G., Royer-Carenzi, M. & Laurin, M. The reconstructed evolutionary process with the fossil record. J. Theor. Biol. 315, 26–37 (2012).

    Article  PubMed  Google Scholar 

  12. Silvestro, D., Warnock, R. C. M., Gavryushkina, A. & Stadler, T. Closing the gap between palaeontological and neontological speciation and extinction rate estimates. Nat. Commun. 9, 5237 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 concepts. J. Theor. Biol. 447, 41–55 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Budd, G. E. & Mann, R. P. The dynamics of stem and crown groups. Sci. Adv. 6, 1626 (2020).

    Article  Google Scholar 

  15. Seward, A. C. The Jurassic flora II. Liassic and Oolitic floras of England. In Catalogue of the Mesozoic plants in the Department of Geology, British Museum (National History) (British Museum, 1904).

  16. Cornet, B. Late Triassic angiosperm-like pollen from the Richmond Rift Basin of Virginia, U.S.A. Palaeontogr. Abt. B 213, 37–87 (1989).

    Google Scholar 

  17. Ren, D. Flower-associated Brachycera flies as fossil evidence for Jurassic angiosperm origins. Science 280, 85–88 (1998).

    Article  CAS  PubMed  Google Scholar 

  18. Cleal, C. J. & Rees, P. M. The Middle Jurassic flora from Stonesfield, Oxfordshire, UK. Palaeontology 46, 739–801 (2003).

    Article  Google Scholar 

  19. Hochuli, P. A. & FeistBurkhardt, S. A boreal early cradle of angiosperms? Angiosperm-like pollen from the Middle Triassic of the Barents Sea (Norway). J. Micropalaeontol. 23, 97–104 (2004).

    Article  Google Scholar 

  20. Hochuli, P. A. & FeistBurkhardt, S. Angiosperm-like pollen and Afropollis from the Middle Triassic (Anisian) of the Germanic Bascin (northern Switzerland). Front. Plant Sci. 4, e344 (2013).

    Article  Google Scholar 

  21. Bell, C. D., Soltis, D. E. & Soltis, P. S. The age and diversification of the angiosperms re-visited. Am. J. Bot. 97, 1296–1303 (2010).

    Article  PubMed  Google Scholar 

  22. Li, H.-T. et al. Origin of angiosperms and the puzzle of the Jurassic gap. Nat. Plants 5, 461–470 (2019).

    Article  PubMed  Google Scholar 

  23. Friis, E. M., Crane, P. R., Pedersen, K. R., Stampanoni, M. & Marone, F. Exceptional preservation of tiny embryos documents seed dormancy in early angiosperms. Nature 528, 551–554 (2018).

    Article  Google Scholar 

  24. Doyle, J. A. Molecular and fossil evidence on the origin of angiosperms. Annu. Rev. Earth Planet. Sci. 40, 301–326 (2012).

    Article  CAS  Google Scholar 

  25. Barba-Montoya, J., dosReis, M., Schneider, H., Donoghue, P. C. J. & Yang, Z. Constraining uncertainty in the timescale of angiosperm evolution and the veracity of a Cretaceous terrestrial revolution. New Phytol. 218, 819–834 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Strauss, D. & Sadler, P. M. Classical confidence-intervals and Bayesian probability estimates for ends of local taxon ranges. Math. Geol. 21, 411–421 (1989).

    Article  Google Scholar 

  27. Marshall, C. R. Confidence-intervals on stratigraphic ranges. Paleobiology 16, 1–10 (1990).

    Article  Google Scholar 

  28. Marshall, C. R. Confidence intervals on stratigraphic ranges with nonrandom distributions of fossil horizons. Paleobiology 23, 165–173 (1997).

    Article  Google Scholar 

  29. Silvestro, D., Salamin, N., Antonelli, A. & Meyer, X. Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework. Paleobiology 45, 546–570 (2019).

    Article  Google Scholar 

  30. 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).

    Article  Google Scholar 

  31. Silvestro, D., Cascales-Miñana, B., Bacon, C. D. & Antonelli, A. Revisiting the origin and diversification of vascular plants through a comprehensive Bayesian analysis of the fossil record. New Phytol. 207, 425–436 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Nowak, H., Schneebeli-Hermann, E. & Kustatscher, E. No mass extinction for land plants at the Permian–Triassic transition. Nat. Commun. 10, 384 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hedman, M. M. Constraints on clade ages from fossil outgroups. Paleobiology 36, 16–31 (2010).

    Article  Google Scholar 

  34. Lloyd, G. T., Bapst, D. W., Friedman, M. & Davis, K. E. Probabilistic divergence time estimation without branch lengths: dating the origins of dinosaurs, avian flight and crown birds. Biol. Lett. 12, 20160609 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Gavryushkina, A. et al. Bayesian total-evidence dating reveals the recent crown radiation of penguins. Syst. Biol. 66, 57–73 (2017).

    PubMed  Google Scholar 

  36. Budd, G. E. & Mann, R. P. History is written by the victors: the effect of the push of the past on the fossil record. Evolution 72, 2276–2291 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Tanner, M. & Wing, H. The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. 82, 528–540 (1987).

    Article  Google Scholar 

  38. Holland, S. M. The non-uniformity of fossil preservation. Phil. Trans. R. Soc. B 371, 20150130 (2016).

    Article  PubMed  Google Scholar 

  39. Pimiento, C. et al. The Pliocene marine megafauna extinction and its impact on functional diversity. Nat. Ecol. Evol. 1, 1100–1106 (2017).

    Article  PubMed  Google Scholar 

  40. Brocklehurst, N., Upchurch, P., Mannion, P. D. & O’Connor, J. The completeness of the fossil record of Mesozoic birds: implications for early avian evolution. PLoS ONE 7, e39056 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Marshall, C. R. Using the fossil record to evaluate timetree timescales. Front. Genet. 10, 449 (2019).

    Article  Google Scholar 

  42. Angiosperm Phylogeny Group et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20 (2016).

    Article  Google Scholar 

  43. Magallón, S., Gòmez-Acevedo, S., Sánchez-Reyes, L. L. & Hernández-Hernández, T. A metacalibrated time-tree documents the early rise of flowering plant phylogenetic diversity. New Phytol. 207, 437–453 (2015).

    Article  PubMed  Google Scholar 

  44. Müller, J. Significance of fossil pollen for angiosperm history. Ann. Mo. Bot. Gard. 71, 419–443 (1984).

    Article  Google Scholar 

  45. Collinson, M. E., Boulter, M. C. & Holmes, P. L. The Fossil Record 2 (ed. Benton, M. J.) 809–841 (Chapman and Hall, 1993).

  46. Doyle, J. A. & Endress, P. K. Integrating Early Cretaceous fossils into the phylogeny of living angiosperms: ANITA lines and relatives of Chloranthaceae. Int. J. Plant Sci. 175, 555–600 (2014).

    Article  Google Scholar 

  47. Doyle, J. A. Recognising angiosperm clades in the Early Cretaceous fossil record. Hist. Biol. 27, 414–429 (2015).

    Article  Google Scholar 

  48. Coiro, M., Martínez, L. C. A., Upchurch, G. R. & Doyle, J. A. Evidence for an extinct lineage of angiosperms from the Early Cretaceous of Patagonia and implications for the early radiation of flowering plants. New Phytol. 228, 344–360 (2020).

    Article  PubMed  Google Scholar 

  49. Beaulieu, J. M., O’Meara, B. C., Crane, P. & Donoghue, M. J. Heterogeneous rates of molecular evolution and diversification could explain the Triassic age estimate for angiosperms. Syst. Biol. 64, 869–878 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Foote, M. On the probability of ancestors in the fossil record. Paleobiology 22, 141–151 (1996).

    Article  Google Scholar 

  52. Drummond, A. J., Ho, S., Phillips, M. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Ronquist, F. et al. A total-evidence approach to dating with fossils, applied to the early radiation of the Hymenoptera. Syst. Biol. 61, 973–999 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  54. van der Kooi, C. J. & Ollerton, J. The origins of flowering plants and pollinators. Science 368, 1306–1308 (2020).

    Article  PubMed  Google Scholar 

  55. Bateman, R. M. Hunting the Snark: the flawed search for mythical Jurassic angiosperms. J. Exp. Bot. 71, 22–35 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. Smith, S. A., Beaulieu, J. M. & Donoghue, M. J. An uncorrelated relaxed-clock analysis suggests an earlier origin for flowering plants. Proc. Natl Acad. Sci. USA 107, 5897–5902 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Zhang, L. et al. The water lily genome and the early evolution of flowering plants. Nature 577, 79–84 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Warnock, R. C. M., Parham, J. F., Joyce, W. G., Tyler, R. L. & Donoghue, P. C. J. Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors. Proc. R. Soc. B 282, 20141013 (2015).

    Article  PubMed  Google Scholar 

  59. Ronquist, F., Lartillot, N. & Phillips, M. J. Closing the gap between rocks and clocks using total-evidence dating. Phil. Trans. R. Soc. B 371, 20150136 (2016).

    Article  PubMed  Google Scholar 

  60. Feild, T. S., Arens, N. C., Doyle, J. A., Dawson, T. E. & Donoghue, M. J. Dark and disturbed: a new image of early angiosperm ecology. Paleobiology 30, 82–107 (2004).

    Article  Google Scholar 

  61. Ramírez-Barahona, S., Sauquet, H. & Magallón, S. The delayed and geographically heterogeneous diversification of flowering plant families. Nat. Ecol. Evol. 4, 1232–1238 (2020).

    Article  PubMed  Google Scholar 

  62. Sokoloff, D. D., Remizowa, M. V., El, E. S., Rudall, P. J. & Bateman, R. M. Supposed Jurassic angiosperms lack pentamery, an important angiosperm-specific feature. New Phytol. 228, 420–426 (2020).

    Article  PubMed  Google Scholar 

  63. Cascales-Miñana, B., Cleal, C. J. & Gerrienne, P. Is Darwin’s ‘abominable mystery’ still a mystery today? Cretac. Res. 61, 256–262 (2016).

    Article  Google Scholar 

  64. Xing, Y. et al. Testing the biases in the rich Cenozoic angiosperm macrofossil record. Int. J. Plant Sci. 177, 371–388 (2016).

    Article  Google Scholar 

  65. Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).

  66. Manchester, S. R., Grímsson, F. & Zetter, R. Assessing the fossil record of asterids in the context of our current phylogenetic framework. Ann. Mo. Bot. Gard. 100, 329–363 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Christenhusz, M. J. M. & Byng, J. W. The number of known plants species in the world and its annual increase. Phytotaxa 261, 201–217 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

We thank R. C. M. Warnock, T. Stadler’s lab and E. Carlisle for feedback on the methods and models presented here. We also thank P. R. Crane for constructive feedback on the manuscript. D.S. received funding from the Swiss National Science Foundation (grant no. PCEFP3_187012) and from the Swedish Research Council (grant no. 2019-04739). A.A. acknowledges financial support from the Swedish Research Council (grant no. 2019-05191), the Swedish Foundation for Strategic Research (grant no. FFL15-0196), the Knut and Alice Wallenberg Foundation (grant no. KAW 2014.0216) and the Royal Botanic Gardens, Kew. Y.X. received funding from the National Natural Science Foundation of China (grant nos 31770226 and U1802242) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB31000000).

Author information

Authors and Affiliations

  1. Department of Biology, University of Fribourg, Fribourg, Switzerland

    Daniele Silvestro & Qiuyue Zhang

  2. Swiss Institute of Bioinformatics, Fribourg, Switzerland

    Daniele Silvestro

  3. Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden

    Daniele Silvestro, Christine D. Bacon & Alexandre Antonelli

  4. Gothenburg Global Biodiversity Centre, Gothenburg, Sweden

    Daniele Silvestro, Christine D. Bacon & Alexandre Antonelli

  5. CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China

    Wenna Ding, Qiuyue Zhang & Yaowu Xing

  6. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

    Qiuyue Zhang

  7. School of Earth Sciences, University of Bristol, Bristol, UK

    Philip C. J. Donoghue

  8. Royal Botanic Gardens, Kew, Richmond, UK

    Alexandre Antonelli

  9. Department of Plant Sciences, University of Oxford, Oxford, UK

    Alexandre Antonelli

Authors
  1. Daniele Silvestro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christine D. Bacon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenna Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiuyue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Philip C. J. Donoghue
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexandre Antonelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yaowu Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.S., C.D.B. and Y.X. conceived the study. W.D., Q.Z. and Y.X. compiled the fossil data. D.S. developed and implemented the methods and analysed the data. D.S. wrote the manuscript with contributions from C.D.B., W.D., Q.Z., P.C.J.D., A.A. and Y.X.

Corresponding author

Correspondence to Daniele Silvestro.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Pamela Soltis, David Cerny and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Relative errors of the estimated Brownian bridge log variances plotted against the relative error of the estimated time of origin based on 200 simulations.

While log variances tended to be slightly underestimated (mostly negative relative errors) they do not have a biasing effect on the estimated times of origin, which show an unbiased error around zero (see also Fig. 2, main text).

Extended Data Fig. 2 Parameter estimates from 200 simulated datasets obtained under MCMC and an approximated MCMC.

In the approximated MCMC, a fraction of the iterations involve no parameter updates (that is qT, a, T, and σ2 do not change), but a new set of conditional Brownian bridges are drawn and accepted as samples from the approximate posterior. This procedure was found to improve the convergence of the MCMC, while having negligible effect on the estimated time of origin a, and sampling rates b, rate trend c, and log variance d,.

Extended Data Fig. 3 Analysis of 200 simulated datasets with random varying sampling rates through time using a BBB model with constant sampling rate (a = 0).

The times of origin were accurately estimated (a); circles and bars indicate posterior estimates and 95% credible intervals. The relative errors on the time of origin were smaller in datasets with richer simulated fossil record (b). The size of the 95% credible intervals around the times of origin decreased with increasing numb er of fossils (c). The log variances were slightly underestimated (d), while the estimated sampling rates (e; the X-axis is log10-transformed) cannot be plotted against true values because the underlying simulations were based on time-heterogeneous sampling with di?erent rates in each time bin. However, we plot for comparison the distribution from which sampling rates were sampled, randomly for each time bin (f; the X-axis is log 10 -transformed).

Extended Data Fig. 4 Analysis of 200 simulated datasets with sampling rates moderately increasing through time using a BBB model with time-varying sampling rates.

The times of origin were underestimated in some cases (a); circles and bars indicate posterior estimates and 95% credible intervals. The relative errors on the time of origin were smaller in datasets with richer simulated fossil record (b). The size of the 95% credible intervals around the times of origin decreased with increasing numb er of fossils (c). The log variances were slightly underestimated (d), while the estimated sampling rates at the time of origin and rate trends (e and f, respectively; the X-axis is log10-transformed) cannot be plotted against true values because they do not have a direct equivalent in the underlying simulations. The distribution from which sampling rates were sampled for each time bin is shown for reference in Extended Data Fig. 3f.

Extended Data Fig. 5 Analysis of 200 simulated datasets with sampling rates strongly increasing through time using a BBB model with time-varying sampling rates.

The times of origin were frequently underestimated (a); circles and bars indicate posterior estimates and 95% credible intervals. The relative errors on the time of origin were smaller in datasets with richer simulated fossil record (b). The size of the 95% credible intervals around the times of origin decreased with increasing numb er of fossils (c). The log variances were slightly underestimated (d), while the estimated sampling rates at the time of origin and rate trends (e and f, respectively; the X-axis is log10-transformed) cannot be plotted against true values because they do not have a direct equivalent in the underlying simulations. The distribution from which sampling rates were sampled for each time bin is shown for reference in Extended Data Fig. 3f.

Extended Data Fig. 6 Analysis of 200 simulated datasets with sampling rates moderately increasing through time using a BBB model with constant sampling rate.

The times of origin were frequently underestimated (a); circles and bars indicate posterior estimates and 95% credible intervals. The relative errors on the time of origin were smaller in datasets with richer simulated fossil record (b). The size of the 95% credible intervals around the times of origin decreased with increasing number of fossils (c). The log variances were slightly underestimated (d), while the estimated sampling rate (e; the X-axis is log10-transformed) cannot be plotted against true values because it does not have a direct equivalent in the underlying simulations. The distribution from which sampling rates were sampled for each time bin is shown for reference in Extended Data Fig. 3f.

Extended Data Fig. 7 Analysis of 200 simulated datasets with sampling rates strongly increasing through time using a BBB model with constant sampling rate.

The times of origin were consistently underestimated (a); circles and bars indicate posterior estimates and 95% CI. The relative errors on the time of origin were smaller in datasets with richer simulated fossil record (b). The size of the 95% credible intervals around the times of origin decreased with increasing number of fossils (c). The log variances were slightly underestimated (d), while the estimated sampling rate (e; the X-axis is log10-transformed) cannot be plotted against true values because it does not have a direct equivalent in the underlying simulations. The distribution from which sampling rates were sampled for each time bin is shown for reference in Extended Data Fig. 3f.

Extended Data Fig. 8 Family-level origination times inferred using bin sizes equal to 1, 2.5, and 5 Myr.

The estimated times of origin and credible intervals were highly consistent across different settings.

Extended Data Fig. 9 Parameters estimated across angiosperm families.

a, Size of the 95% credible intervals for the estimated time of origin of angiosperm families plotted against the number of fossils available: the relationship reflects the observations based on simulated data. Increasing number of fossils results in substantially smaller credible intervals. b, Distributions of estimated variances of the Brownian bridge (σ2; log-scale), c, sampling rates at the time of origin (qT; log-scale), and d, sampling temporal trend (a; log-scale) as inferred across angiosperm families.

Extended Data Fig. 10 Estimated origination times across angiosperm families.

a, Posterior samples of the oldest time of origin across all families obtained after combining the estimated ages of each. The red line indicates the boundary between the Jurassic and the Cretaceous. Only 0.2% of the samples fall within the Cretaceous providing strong statistical evidence for an earlier origin of crown angiosperm. b, Root age estimates of extant families of angiosperm with 95% credible intervals (left) as inferred from meso- and macrofossils only, excluding pollen data and cumulative family diversity (right) based on those estimates (Y-axis is log10 transformed). The analyses we run under a BBB model with time-increasing sampling rates. c, Root age estimates of extant families of angiosperm with 95% credible intervals (left) as inferred from a BBB model with sampling rate set to be constant (parameter a = 0) and cumulative family diversity (right) based on those estimates (Y-axis is log10-transformed).

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Table 3

Fossil occurrences included in the analyses with taxonomic classifications, age ranges and references.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Silvestro, D., Bacon, C.D., Ding, W. et al. Fossil data support a pre-Cretaceous origin of flowering plants. Nat Ecol Evol 5, 449–457 (2021). https://doi.org/10.1038/s41559-020-01387-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-01387-8

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing