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The origin and evolution of open habitats in North America inferred by Bayesian deep learning models

Author

Listed:
  • Tobias Andermann

    (SciLifeLab, Uppsala University
    University of Gothenburg
    University of Gothenburg)

  • Caroline A. E. Strömberg

    (University of Washington)

  • Alexandre Antonelli

    (University of Gothenburg
    University of Gothenburg
    University of Oxford
    Royal Botanic Gardens, Kew, Richmond)

  • Daniele Silvestro

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

Abstract

Some of the most extensive terrestrial biomes today consist of open vegetation, including temperate grasslands and tropical savannas. These biomes originated relatively recently in Earth’s history, likely replacing forested habitats in the second half of the Cenozoic. However, the timing of their origination and expansion remains disputed. Here, we present a Bayesian deep learning model that utilizes information from fossil evidence, geologic models, and paleoclimatic proxies to reconstruct paleovegetation, placing the emergence of open habitats in North America at around 23 million years ago. By the time of the onset of the Quaternary glacial cycles, open habitats were covering more than 30% of North America and were expanding at peak rates, to eventually become the most prominent natural vegetation type today. Our entirely data-driven approach demonstrates how deep learning can harness unexplored signals from complex data sets to provide insights into the evolution of Earth’s biomes in time and space.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32300-5
    DOI: 10.1038/s41467-022-32300-5
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    References listed on IDEAS

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    Cited by:

    1. 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.

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