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

Cristina Cipriano, Sergio Noce, Simone Mereu and Monia Santini

Ecological Modelling, 2025, vol. 506, issue C

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.

Keywords: Machine learning; Deep learning; Reinforcement learning; Semi-supervised learning; Unsupervised learning; Supervised learning; Ecology (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:506:y:2025:i:c:s0304380025001498

DOI: 10.1016/j.ecolmodel.2025.111164

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