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DeepDive: estimating global biodiversity patterns through time using deep learning

Author

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  • Rebecca B. Cooper

    (University of Fribourg
    Swiss Institute of Bioinformatics)

  • Joseph T. Flannery-Sutherland

    (University of Birmingham)

  • Daniele Silvestro

    (University of Fribourg
    Swiss Institute of Bioinformatics
    University of Gothenburg)

Abstract

Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

Suggested Citation

  • Rebecca B. Cooper & Joseph T. Flannery-Sutherland & Daniele Silvestro, 2024. "DeepDive: estimating global biodiversity patterns through time using deep learning," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48434-7
    DOI: 10.1038/s41467-024-48434-7
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    References listed on IDEAS

    as
    1. Joseph T. Flannery-Sutherland & Daniele Silvestro & Michael J. Benton, 2022. "Global diversity dynamics in the fossil record are regionally heterogeneous," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Tobias Andermann & Caroline A. E. Strömberg & Alexandre Antonelli & Daniele Silvestro, 2022. "The origin and evolution of open habitats in North America inferred by Bayesian deep learning models," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Daril A. Vilhena & Alexandre Antonelli, 2015. "A network approach for identifying and delimiting biogeographical regions," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    4. Jennifer F. Hoyal Cuthill & Nicholas Guttenberg & Graham E. Budd, 2020. "Impacts of speciation and extinction measured by an evolutionary decay clock," Nature, Nature, vol. 588(7839), pages 636-641, December.
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