June 26, 2026

Strike Force heroes4

Connecting the World with Advanced Technology

Emerging uses of artificial intelligence in deep time biodiversity research

Emerging uses of artificial intelligence in deep time biodiversity research
  • Raup, D. M. & Sepkoski, J. J. Mass extinctions in the marine fossil record. N. Ser. 215, 1501–1503 (1982).

    CAS 

    Google Scholar 

  • Benton, M. J. Recovery of vertebrate faunas from the end-Permian mass extinction. J. Earth Sci. 21 111 (2010).

    Google Scholar 

  • Benton, M. J. Origins of biodiversity. PLoS Biol. (2016).

  • Marshall, C. R. Forty years later: the status of the ‘Big Five’ mass extinctions. Camb. Prism Extinct. 1, e5 (2023).

    Google Scholar 

  • Casanovas-Vilar, I., van den Hoek Ostende, L. W., Janis, C. M. & Saarinen, J. eds. Evolution of Cenozoic Land Mammal Faunas and Ecosystems 25 Years of the NOW Database of Fossil Mammals Vertebrate Paleobiology and Paleoanthropology Series (Springer, 2023).

  • Uhen, M. D. et al. Paleobiology Database User Guide Version 1.0. PaleoBios 40(11) (2023).

  • Peters, S. E. & McClennen, M. The Paleobiology Database Application Programming Interface. Paleobiology 42, 1–7 (2015).

    Google Scholar 

  • Chiappe, L. M. et al. Cretaceous bird from Brazil informs the evolution of the avian skull and brain. Nature 635, 376–381 (2024).

    CAS 

    Google Scholar 

  • Niklas, K. J., Tiffany, B. H. & Knoll, A. H. Patterns in vascular land plant diversification. Nature 303, 1068–1070 (1983).

    Google Scholar 

  • Niklas, K. J. Measuring the tempo of plant death and birth. N. Phytol. 207, 254–256 (2015).

    Google Scholar 

  • Sepkoski, J. J. A factor analytic description of the Phanerozoic marine fossil record. Paleobiology 7 36–53 (1981).

    Google Scholar 

  • Dunne, E. M., Thompson, S. E. D., Butler, R. J., Rosindell, J. & Close, R. A. Mechanistic neutral models show that sampling biases drive the apparent explosion of early tetrapod diversity. Nat. Ecol. Evol. 7, 1480–1489 (2023).

    Google Scholar 

  • Close, R. A., Benson, R. B. J., Saupe, E. E., Clapham, M. E. & Butler, R. J. The spatial structure of Phanerozoic marine animal diversity. Science 368, 420–424 (2020).

    CAS 

    Google Scholar 

  • Reijenga, B. R. & Close, R. A. Apparent timescaling of fossil diversification rates is caused by sampling bias. Curr. Biol. (2025).

    Google Scholar 

  • Marshall, C. R. et al. Quantifying the dark data in museum fossil collections as palaeontology undergoes a second digital revolution. Biol. Lett. 14, 20180431 (2018).

    Google Scholar 

  • Adaimé, M.-É., Urban, M. A., Kong, S., Jaramillo, C. & Punyasena, S. W. Pollen morphology, deep learning, phylogenetics, and the evolution of environmental adaptations in Podocarpus. New Phytol. 247, 1460–1473 (2025).

    Google Scholar 

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    CAS 

    Google Scholar 

  • Tropsha, A., Isayev, O., Varnek, A., Schneider, G. & Cherkasov, A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat. Rev. Drug Discov. 23, 141–155 (2023).

    Google Scholar 

  • Müller, J. et al. Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests. Nat. Commun. 14, 6191 (2023).

    Google Scholar 

  • Romera-Paredes, B. et al. Mathematical discoveries from program search with large language models. Nature 625, 468–475 (2023).

    Google Scholar 

  • Thompson, T. How AI can help to save endangered species. Nature 623, 232–233 (2023).

    CAS 

    Google Scholar 

  • Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5, 415–424 (2022).

    Google Scholar 

  • Christin, S., Hervet, É. & Lecomte, N. Applications for deep learning in ecology. Methods Ecol. Evol. 10, 1632–1644 (2019).

    Google Scholar 

  • Yu, C. et al. Artificial intelligence in paleontology. Earth Sci. Rev. 252, 104765 (2024).

    Google Scholar 

  • He, Y. et al. Opportunities and challenges in applying AI to evolutionary morphology. Integr. Org. Biol. 6, 36 (2024).

    Google Scholar 

  • Mimura, K. et al. Applicability of object detection to microfossil research: implications from deep learning models to detect microfossil fish teeth and denticles using YOLO-v7. Earth Space Sci. 11, e2023EA003122 (2024).

    Google Scholar 

  • van de Kamp, T. et al. Parasitoid biology preserved in mineralized fossils. Nat. Commun. 9, 3325 (2018).

    Google Scholar 

  • Romero, I. C. et al. Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy. Proc. Natl Acad. Sci. USA 117, 28496–28505 (2020).

    CAS 

    Google Scholar 

  • Kopperud, B. T., Lidgard, S. & Liow, L. H. Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps. PeerJ 10, e13921 (2022).

    Google Scholar 

  • Di Martino, E. et al. DeepBryo: a web app for AI-assisted morphometric characterization of cheilostome bryozoans. Limnol. Oceanogr. Methods 21, 542–551 (2023).

    Google Scholar 

  • Liu, X. et al. Heterogeneous selectivity and morphological evolution of marine clades during the Permian–Triassic mass extinction. Nat. Ecol. Evol. 8, 1248–1258 (2024).

    Google Scholar 

  • Weeks, B. C. et al. A deep neural network for high-throughput measurement of functional traits on museum skeletal specimens. Methods Ecol. Evol. 14, 347–359 (2023).

    Google Scholar 

  • Hou, C. et al. Fossil image identification using deep learning ensembles of data augmented multiviews. Methods Ecol. Evol. 14, 3020–3034 (2023).

    Google Scholar 

  • Foster, W. J. et al. How predictable are mass extinction events? R. Soc. Open Sci. 10, 221507 (2023).

    Google Scholar 

  • Foster, W. J. et al. Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology 48, 357–371 (2022).

    Google Scholar 

  • Finnegan, S. et al. Paleontological baselines for evaluating extinction risk in the modern oceans. Science 348, 567–570 (2015).

    CAS 

    Google Scholar 

  • Cooper, R. B., Flannery-Sutherland, J. T. & Silvestro, D. DeepDive: estimating global biodiversity patterns through time using deep learning. Nat. Commun. 15, 4199 (2024).

    CAS 

    Google Scholar 

  • Nickolls, J. & Dally, W. J. The GPU computing era. IEEE Micro (2010).

    Google Scholar 

  • Vaswani, A. et al.Attention is all you need. In Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) (2017).

  • Koch, B., Denton, E., Hanna, A. & Foster, J. G. Reduced, reused and recycled: the life of a dataset in machine learning research. Preprint at (2021).

  • Villalobos, P. et al. Will we run out of data? Limits of LLM scaling based on human-generated data. Preprint at (2024).

  • Sayers, E. W. et al. GenBank 2025 update. Nucleic Acids Res. 53, D56–D61 (2025).

    Google Scholar 

  • Waller, J. Citizen Science on GBIF – 2019. GBIF Data Blog (2019).

  • Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B. & Schigel, D. Data integration enables global biodiversity synthesis. Proc. Natl Acad. Sci. USA 118, e2018093118 (2021).

    CAS 

    Google Scholar 

  • Renaudie, J., Lazarus, D. B. & Diver, P. Nsb (Neptune Sandbox Berlin): an expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy. Palaeontol. Electron. 23, 1–28 (2020).

    Google Scholar 

  • Žliobaitė, I. et al. The NOW database of fossil mammals. Vertebr. Paleobiol. Paleoanthropol. F1250, 33–42 (2023) .

    Google Scholar 

  • Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The R package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).

    Google Scholar 

  • Smith, J. et al. BioDeepTime: a database of biodiversity time series for modern and fossil assemblages. Glob. Ecol. Biogeogr. 32, 1680–1689 (2023).

    Google Scholar 

  • Smith, J. A. et al. Increasing the equitability of data citation in paleontology: capacity building for the big data future. Paleobiology 50, 165–176 (2024).

    Google Scholar 

  • Nicol, D. The number of living animal species likely to be fossilized. Fla. Scientist 40, 135–139 (1977).

    Google Scholar 

  • Foote, M., Miller, A. I., Raup, D. M. & Stanley, S. M. Principles of Paleontology (Macmillan, 2007).

  • Andermann, T., Antonelli, A., Barrett, R. L. & Silvestro, D. Estimating alpha, beta, and gamma diversity through deep learning. Front. Plant Sci. 13, 839407 (2022).

    Google Scholar 

  • Zhuang, F. et al. A comprehensive survey on transfer learning. Proc. IEEE 109, 43–76 (2021).

    Google Scholar 

  • Kim, H. E. et al. Transfer learning for medical image classification: a literature review. BMC Med. Imaging 22, 69 (2022).

    Google Scholar 

  • Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    CAS 

    Google Scholar 

  • Hauffe, T., Cantalapiedra, J. L. & Silvestro, D. Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks. Sci. Adv. 10, eadl2643 (2024).

    Google Scholar 

  • Marjoram, P., Molitor, J., Plagnol, V. & Tavaré, S. Markov chain Monte Carlo without likelihoods. Proc. Natl Acad. Sci. USA 100, 15324–15328 (2003).

    CAS 

    Google Scholar 

  • Silvestro, D. et al. A 450 million years long latitudinal gradient in age-dependent extinction. Ecol. Lett. 23, 439–446 (2020).

    Google Scholar 

  • Lambert, S., Voznica, J. & Morlon, H. Deep learning from phylogenies for diversification analyses. Syst. Biol. 72, 1262–1279 (2023).

    Google Scholar 

  • Close, R. A. et al. Diversity dynamics of Phanerozoic terrestrial tetrapods at the local-community scale. Nat. Ecol. Evol. 3, 590–597 (2019).

    Google Scholar 

  • Ahmad, W., Ali, H., Shah, Z. & Azmat, S. A new generative adversarial network for medical images super resolution. Sci. Rep. 12, 1–20 (2022).

    Google Scholar 

  • Nie, D. et al. Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. 65, 2720–2730 (2018).

    Google Scholar 

  • Khosravi, B. et al. Few-shot biomedical image segmentation using diffusion models: beyond image generation. Comput. Methods Prog. Biomed. 242, 107832 (2023).

    Google Scholar 

  • Huang, Y. et al. SmartEdit: exploring complex instruction-based image editing with multimodal large language models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (IEEE, 2023).

  • Rawte, V., Sheth, A. & Das, A. A survey of hallucination in large foundation models. Preprint at (2023).

  • Zhang, Y. et al. Siren’s song in the AI ocean: a survey on hallucination in large language models. Comput. Linguist. (2025).

  • Bai, Y. et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. Preprint at (2022).

  • Bagenal, J. Generative artificial intelligence and scientific publishing: urgent questions, difficult answers. Lancet 403, 1118–1120 (2024).

    Google Scholar 

  • link

    Copyright © All rights reserved. | Newsphere by AF themes.