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Algorithms going wild – A review of machine learning techniques for terrestrial ecology

Author

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  • Cipriano, Cristina
  • Noce, Sergio
  • Mereu, Simone
  • Santini, Monia

Abstract

The integration of artificial intelligence (AI) algorithms in ecological research is revolutionizing how we monitor, predict, and manage natural systems, enabling more advanced data analysis, pattern recognition, and predictive modelling. This review critically analyzes and synthesizes the application of machine learning and deep learning in terrestrial ecology, providing a comprehensive overview of their paradigms – namely unsupervised, supervised, and reinforcement learning – and semi-supervised learning, along with their respective algorithm families, strengths, and limitations. We examine both current and emerging applications in terrestrial ecological dynamics and modelling, ecosystem management and conservation, identification and classification tasks, such as trait and behavior recognition. Despite these advancements, we summarize several issues hindering the extensive adoption of AI algorithms in ecology, such as inconsistencies or limitations in datasets, algorithm complexity and interpretability affecting transparency and reliability, high computational demands raising environmental sustainability concerns, and difficulties with model generalization. To address these barriers, we identify key areas for future research, namely optimizing data collection, using transfer learning and data augmentation, refining model transparency through explainable AI (XAI) and ethical considerations, and integrating causal inference into AI models. We conclude that AI algorithms hold great promise for delivering more accurate, scalable, and timely data, advancing real-time monitoring and near-instantaneous predictions – e.g., seasonal forecasting – for more dynamic responses to environmental changes. The need for continued methodological innovation and multi- and trans-disciplinary collaboration is emphasized to ensure these technologies are effective, sustainable, and equitable in supporting ecosystem conservation and restoration efforts addressing global ecological crises.

Suggested Citation

  • Cipriano, Cristina & Noce, Sergio & Mereu, Simone & Santini, Monia, 2025. "Algorithms going wild – A review of machine learning techniques for terrestrial ecology," Ecological Modelling, Elsevier, vol. 506(C).
    • Handle: RePEc:eee:ecomod:v:506:y:2025:i:c:s0304380025001498
    DOI: 10.1016/j.ecolmodel.2025.111164
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    1. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    3. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    4. Silva, C.A. & Vilaça, R. & Pereira, A. & Bessa, R.J., 2024. "A review on the decarbonization of high-performance computing centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    5. Rondon, Diego & Mäntyniemi, Samu & Aspi, Jouni & Kvist, Laura & Sillanpää, Mikko J., 2024. "A Bayesian multi-state model with data augmentation for estimating population size and effect of inbreeding on survival," Ecological Modelling, Elsevier, vol. 490(C).
    6. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    7. repec:plo:pone00:0193085 is not listed on IDEAS
    8. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Yen-Lin Chen, 2022. "Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    9. Thomas J. Stohlgren & Peter Ma & Sunil Kumar & Monique Rocca & Jeffrey T. Morisette & Catherine S. Jarnevich & Nate Benson, 2010. "Ensemble Habitat Mapping of Invasive Plant Species," Risk Analysis, John Wiley & Sons, vol. 30(2), pages 224-235, February.
    10. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    11. Maya Wardeh & Matthew Baylis & Marcus S. C. Blagrove, 2021. "Predicting mammalian hosts in which novel coronaviruses can be generated," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    12. repec:plo:pone00:0235750 is not listed on IDEAS
    13. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    14. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    15. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    16. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    17. Tobias Jensen & Frederik Seerup Hass & Mohammad Seam Akbar & Philip Holm Petersen & Jamal Jokar Arsanjani, 2020. "Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
    18. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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