ArticleNature communications2022
The origin and evolution of open habitats in North America inferred by Bayesian deep learning models.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
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- New Miocene litoptern remains from Colombia and ecological structure of American Neogene herbivore guilds.BMC zoology · 2025Article
- Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks.Science advances · 2024Article
- DeepDive: estimating global biodiversity patterns through time using deep learning.Nature communications · 2024Article
- The origin and evolution of open habitats in North America inferred by Bayesian deep learning models.Nature communications · 2022Article
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4 authors.
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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.
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