Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Progress
  • Published:

Generative AI as a tool to accelerate the field of ecology

Nature Ecology & Evolution volume 9, pages 378–385 (2025)Cite this article

Subjects

Abstract

The emergence of generative artificial intelligence (AI) models specializing in the generation of new data with the statistical patterns and properties of the data upon which the models were trained has profoundly influenced a range of academic disciplines, industry and public discourse. Combined with the vast amounts of diverse data now available to ecologists, from genetic sequences to remotely sensed animal tracks, generative AI presents enormous potential applications within ecology. Here we draw upon a range of fields to discuss unique potential applications in which generative AI could accelerate the field of ecology, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data. We also highlight key challenges, risks and considerations when using generative AI within ecology, such as privacy risks, model biases and environmental effects. Ultimately, the future of generative AI in ecology lies in the development of robust interdisciplinary collaborations between ecologists and computer scientists. Such partnerships will be important for embedding ecological knowledge within AI, leading to more ecologically meaningful and relevant models. This will be critical for leveraging the power of generative AI to drive ecological insights into species across the globe.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Tuia, D. et al. Perspectives in machine learning for wildlife conservation. Nat. Commun. 13, 792 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl Acad. Sci. USA 115, E5716–E5725 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pichler, M., Boreux, V., Klein, A.-M., Schleuning, M. & Hartig, F. Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks. Methods Ecol. Evol. 11, 281–293 (2020).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Walraven, E., Spaan, M. T. J. & Bakker, B. Traffic flow optimization: a reinforcement learning approach. Eng. Appl. Artif. Intell. 52, 203–212 (2016).

    Article  Google Scholar 

  7. Shahriar, S. et al. Putting GPT-4o to the sword: a comprehensive evaluation of language, vision, speech, and multimodal proficiency. Appl. Sci. 14, 7782 (2024).

    Article  CAS  Google Scholar 

  8. Tian, Y. et al. StableRep: synthetic images from text-to-image models make strong visual representation learners. In Proc. 37th International Conference on Neural Information Processing Systems 48382–48402 (Curran Associates, 2024).

  9. Esser, P. et al. Structure and content-guided video synthesis with diffusion models in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) 7312–7322 (IEEE, 2023).

  10. Naveed, H. et al. A comprehensive overview of large language models. Preprint at arXiv https://doi.org/10.48550/arXiv.2307.06435 (2024).

  11. Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 382, 1416–1421 (2023).

    Article  CAS  PubMed  Google Scholar 

  12. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Article  CAS  PubMed  Google Scholar 

  13. Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Sugai, L. S. M., Silva, T. S. F., Ribeiro, J. W. Jr & Llusia, D. Terrestrial passive acoustic monitoring: review and perspectives. BioScience 69, 15–25 (2019).

    Article  Google Scholar 

  15. Beery, S. in AI for Social Impact (eds Tambe, M. et al.) https://www.ai4sibook.org/wp-content/uploads/2022/08/MegaDetector.pdf (2022).

  16. Hart, A. G. et al. Assessing the accuracy of free automated plant identification applications. People Nat. 5, 929–937 (2023).

    Article  Google Scholar 

  17. Beery, S., Van Horn, G. & Perona, P. Recognition in terra incognita. In Proc. European Conference on Computer Vision (eds. Ferrari, V. et al.) 456–473 (2018).

  18. Koh, P. W. et al. WILDS: a benchmark of in-the-wild distribution shifts. In Proc. 38th International Conference on Machine Learning, 139 (ICML, 2021).

  19. van Dyk, D. A. & Meng, X.-L. The art of data augmentation. J. Comput. Graph. Stat. 10, 1–50 (2001).

    Article  Google Scholar 

  20. Hittmeir, M., Ekelhart, A. & Mayer, R. On the utility of synthetic data: an empirical evaluation on machine learning tasks. In ARES '19: Proceedings of the 14th International Conference on Availability, Reliability and Security, article no. 29 (2019).

  21. Goodfellow, I. et al. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems 2672–2680 (Curran Associates, 2014).

  22. Dhariwal, P. & Nichol, A. Diffusion models beat GANs on image synthesis. In Proc. 35th International Conference on Neural Information Processing Systems, https://doi.org/10.5555/3540261.3540933 (Curran Associates, 2021).

  23. Azizi, S. et al. Synthetic data from diffusion models improves ImageNet classification. Trans. Mach. Learn. Res. (2023).

  24. Beery, S. et al. Synthetic examples improve generalization for rare classes. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) 852–862 (IEEE, 2020).

  25. Vendrow, J., Jain, S., Engstrom, L. & Madry, A. Dataset interfaces: diagnosing model failures using controllable counterfactual generation. In Data-Centric Machine Learning Research (DMLR) Workshop at ICML 2023, https://dmlr.ai/assets/accepted-papers/49/CameraReady/icml.pdf (2023).

  26. Ghiasi, G. et al. Simple copy-paste is a strong data augmentation method for instance segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), https://doi.org/10.1109/CVPR46437.2021.00294 (IEEE, 2021).

  27. Lu, C.-Y., Arcega Rustia, D. J. & Lin, T.-T. Generative adversarial network based image augmentation for insect pest classification enhancement. IFAC Pap. 52, 1–5 (2019).

    CAS  Google Scholar 

  28. Schwartz, I. et al. Discriminative class tokens for text-to-image diffusion models. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) 22668–22678 (IEEE, 2023).

  29. Werber, Y., Sextin, H., Yovel, Y. & Sapir, N. BATScan: a radar classification tool reveals large-scale bat migration patterns. Meth. Ecol. Evol. 14, 1764–1779 (2023).

    Article  Google Scholar 

  30. Wan, Z. & Dodge, S. A generative trajectory interpolation method for imputing gaps in wildlife movement data. In GeoWildLife 23 Proc. 1st ACM SIGSPATIAL International Workshop AI-Driven Spatio-Temporal Data Analysis Wildlife Conservation 1–8 (ACM, 2023).

  31. Luleci, F., Catbas, F. N. & Avci, O. Generative adversarial networks for labeled acceleration data augmentation for structural damage detection. J. Civ. Struct. Health Monit. 13, 181–198 (2023).

    Article  Google Scholar 

  32. Jiguet, F. et al. Multi-sensor data loggers identify the location and timing in four poaching cases of the endangered Eurasian Curlew Numenius arquata. Forensic Sci. Int. Anim. Environ. 4, 100069 (2023).

    Article  Google Scholar 

  33. Gal, R. et al. An image is worth one word: personalizing text-to-image generation using textual inversion. In Proc. 11th International Conference on Learning Representations, https://openreview.net/forum?id=NAQvF08TcyG (2023).

  34. Yuan, J. et al. Real-fake: effective training data synthesis through distribution matching. In Proc. 12th International Conference on Learning Representations, https://openreview.net/pdf?id=svIdLLZpsA (2023).

  35. Hirn, J. et al. A deep generative artificial intelligence system to predict species coexistence patterns. Meth. Ecol. Evol. 13, 1052–1061 (2022).

    Article  Google Scholar 

  36. Borowiec, M. L. et al. Deep learning as a tool for ecology and evolution. Meth. Ecol. Evol. 13, 1640–1660 (2022).

    Article  Google Scholar 

  37. Chesson, P. General theory of competitive coexistence in spatially-varying environments. Theor. Popul. Biol. 58, 211–237 (2000).

    Article  CAS  PubMed  Google Scholar 

  38. Roy, A., Fablet, R. & Bertrand, S. L. Using generative adversarial networks (GAN) to simulate central-place foraging trajectories. Meth. Ecol. Evol. 13, 1275–1287 (2022).

    Article  Google Scholar 

  39. Peck, S. L. Simulation as experiment: a philosophical reassessment for biological modeling. Trends Ecol. Evol. 19, 530–534 (2004).

    Article  PubMed  Google Scholar 

  40. Hirn, J., Sanz, V. & Verdú, M. in Generative AI to Understand Complex Ecological Interactions 293–308 (Springer, 2024).

  41. DeAngelis, D. L. & Diaz, S. G. Decision-making in agent-based modeling: a current review and future prospectus. Front. Ecol. Evol. 6, 237 (2019).

    Article  Google Scholar 

  42. Ghaffarzadegan, N., Majumdar, A., Williams, R. & Hosseinichimeh, N. Generative agent-based modeling: an introduction and tutorial. Syst. Dyn. Rev. 40, e1761 (2024).

    Article  Google Scholar 

  43. Williams, R., Hosseinichimeh, N., Majumdar, A. & Ghaffarzadegan, N. Epidemic modeling with generative agents. Preprint at arXiv https://doi.org/10.48550/arXiv.2307.04986 (2023).

  44. Park, J. S. et al. Generative agents: interactive simulacra of human behavior. In Proc. 36th Annual ACM Symposium on User Interface Software and Technology 1–22 (Association for Computing Machinery, 2023).

  45. Potts, J. R. & Lewis, M. A. How memory of direct animal interactions can lead to territorial pattern formation. J. R. Soc. Interface 13, 20160059 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Strandburg-Peshkin, A., Farine, D. R., Crofoot, M. C. & Couzin, I. D. Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. eLife 6, e19505 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Han, B. A. et al. A synergistic future for AI and ecology. Proc. Natl Acad. Sci. USA 120, e2220283120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Abrahms, B. et al. Climate change as a global amplifier of human–wildlife conflict. Nat. Clim. Change 13, 224–234 (2023).

    Article  Google Scholar 

  49. Hennessy, A., Clarke, K. & Lewis, M. Generative adversarial network synthesis of hyperspectral vegetation data. Remote Sens. 13, 2243 (2021).

    Article  Google Scholar 

  50. Requena-Mesa, C. et al. in Pattern Recognition (eds Fink, G. A. et al.) 203–217 (Springer, 2019).

  51. Dinnage, R. How many variables does Wordclim have, really? Generative A.I. unravels the intrinsic dimension of bioclimatic variables. Preprint at bioRxiv https://doi.org/10.1101/2023.06.12.544623 (2023).

  52. Castro, A., Pinto, J., Reino, L., Pipek, P. & Capinha, C. Large language models overcome the challenges of unstructured text data in ecology. Ecol. Inform. 82, 102742 (2024).

    Article  Google Scholar 

  53. Gao, Y. et al. Retrieval-augmented generation for large language models: a survey. Preprint at arXiv https://doi.org/10.48550/arXiv.2312.10997 (2024).

  54. Cooper, N. et al. Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution. Methods Ecol. Evol. 15, 1757–1763 (2024).

    Article  Google Scholar 

  55. Suri, S. Defining our future with generative AI. Nat. Comput. Sci. 4, 641–643 (2024).

    Article  PubMed  Google Scholar 

  56. Ferrara, E. Should ChatGPT be biased? Challenges and risks of bias in large language models. First Monday 28, 11 (2023).

    Google Scholar 

  57. Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A. & Kersting, K. Large pre-trained language models contain human-like biases of what is right and wrong to do. Nat. Mach. Intell. 4, 258–268 (2022).

    Article  Google Scholar 

  58. Konno, K. et al. Ignoring non-English-language studies may bias ecological meta-analyses. Ecol. Evol. 10, 6373–6384 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Maas, B. et al. Women and global south strikingly underrepresented among top-publishing ecologists. Conserv. Lett. 14, e12797 (2021).

    Article  Google Scholar 

  60. Clark, J. A. & May, R. M. Taxonomic bias in conservation research. Science 297, 191–192 (2002).

    Article  CAS  PubMed  Google Scholar 

  61. Hughes, A. C. et al. Who is publishing in ecology and evolution? The underrepresentation of women and the Global South. Front. Environ. Sci. 11, 1211211 (2023).

    Article  Google Scholar 

  62. Azamfirei, R., Kudchadkar, S. R. & Fackler, J. Large language models and the perils of their hallucinations. Crit. Care 27, 120 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Boyko, J. et al. An interdisciplinary outlook on large language models for scientific research. Preprint at arXiv https://doi.org/10.48550/arXiv.2311.04929 (2023).

  64. Gupta, U. et al. Chasing carbon: the elusive environmental footprint of computing. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) 854–867 (IEEE, 2021).

  65. Chien, A. A. et al. Reducing the carbon impact of generative AI inference (today and in 2035). In Proc. 2nd Workshop on Sustainable Computer Systems 1–7 (ACM, 2023).

  66. Crawford, K. Generative AI’s environmental costs are soaring — and mostly secret. Nature 626, 693 (2024). –693.

    Article  CAS  PubMed  Google Scholar 

  67. Elkin-Koren, N., Hacohen, U., Livni, R. & Moran, S. Can copyright be reduced to privacy? In 5th Symposium on Foundations of Responsible Computing (FORC 2024) 3:1–3:18 (Schloss Dagstuhl – Leibniz-Zentrum fürInformatik, 2024).

  68. Smits, J. & Borghuis, T. in Law and Artificial Intelligence: Regulating AI and Applying AI in Legal Practice (eds Custers, B. & Fosch-Villaronga, E.) 323–344 (T.M.C. Asser Press, 2022).

  69. Huang, J., Shao, H. & Chang, K. C.-C. Are large pre-trained language models leaking your personal information? In Findings of the Association for Computational Linguistics 2038–2047 (Association for Computational Linguistics, 2022).

  70. Wang, R. et al. Applications of generative adversarial networks in neuroimaging and clinical neuroscience. NeuroImage 269, 119898 (2023).

    Article  PubMed  Google Scholar 

  71. Frazer, J. et al. Disease variant prediction with deep generative models of evolutionary data. Nature 599, 91–95 (2021).

    Article  CAS  PubMed  Google Scholar 

  72. Li, L., Carver, R., Lopez-Gomez, I., Sha, F. & Anderson, J. Generative emulation of weather forecast ensembles with diffusion models. Sci. Adv. 10, eadk4489 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Yelmen, B. et al. Creating artificial human genomes using generative neural networks. PLoS Genet. 17, e1009303 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Chen, Y. et al. Generative adversarial networks in medical image augmentation: a review. Comput. Biol. Med. 144, 105382 (2022).

    Article  PubMed  Google Scholar 

  75. Thukral, V., Latvala, L., Swenson, M. & Horn, J. Customer journey optimisation using large language models: best practices and pitfalls in generative AI. Appl. Mark. Anal. 9, 281–292 (2023).

    Google Scholar 

  76. Guo, E. et al. Automated paper screening for clinical reviews using large language models. J. Med. Internet Res. https://doi.org/10.2196/48996 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  77. AJE. AJE: English editing and author services for research publishers. AJE https://www.aje.com/ (AJE, 2024).

  78. Gaia, G., Boiano, S. & Borda, A. in Museums and Digital Culture: New Perspectives and Research (eds Giannini, T. & Bowen, J. P.) 309–329 (Springer International, 2019).

  79. Richards, D., Worden, D., Song, X. P. & Lavorel, S. Harnessing generative artificial intelligence to support nature-based solutions. People Nat. 6, 882–893 (2024).

    Article  Google Scholar 

  80. Bond-Taylor, S., Leach, A., Long, Y. & Willcocks, C. G. Deep generative modelling: a comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7327–7347 (2022).

    Article  PubMed  Google Scholar 

  81. Gm, H., Gourisaria, M. K., Pandey, M. & Rautaray, S. S. A comprehensive survey and analysis of generative models in machine learning. Comput. Sci. Rev. 38, 100285 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

We thank U. Paquet for providing early guidance and feedback on the scope of the manuscript and our institutions for supporting our time in preparing the manuscript. K.R. was supported by the University of Washington eScience Postdoctoral Fellowship and the Washington Research Foundation. Z.H. was funded by the National Science Foundation (grant CCF 2019844).

Author information

Authors and Affiliations

  1. Center for Ecosystem Sentinels, Department of Biology, University of Washington, Seattle, WA, USA

    Kasim Rafiq & Briana Abrahms

  2. AI and Decision Making, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sara Beery

  3. Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA

    Meredith S. Palmer

  4. Allen School in Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Zaid Harchaoui

Authors
  1. Kasim Rafiq
    View author publications

    Search author on:PubMed Google Scholar

  2. Sara Beery
    View author publications

    Search author on:PubMed Google Scholar

  3. Meredith S. Palmer
    View author publications

    Search author on:PubMed Google Scholar

  4. Zaid Harchaoui
    View author publications

    Search author on:PubMed Google Scholar

  5. Briana Abrahms
    View author publications

    Search author on:PubMed Google Scholar

Contributions

K.R. conceptualized and led the paper. K.R., B.A., M.S.P. and S.B. contributed to the original draft of the manuscript. K.R., B.A., M.S.P., S.B. and Z.H. provided critical feedback, edits and revisions to the manuscript.

Corresponding author

Correspondence to Kasim Rafiq.

Peer review

Peer review information

Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rafiq, K., Beery, S., Palmer, M.S. et al. Generative AI as a tool to accelerate the field of ecology. Nat Ecol Evol 9, 378–385 (2025). https://doi.org/10.1038/s41559-024-02623-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41559-024-02623-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene