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Fossil data support a pre-Cretaceous origin of flowering plants

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

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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.

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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.

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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.

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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

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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.

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Correspondence to Daniele Silvestro.

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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.

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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.

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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

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