Machine learning in ecology: Current status and future directions in the big data era

Ecology is entering a data-rich era driven by rapid advances in sensor technologies and the expansion of environmental observations across spatial and temporal scales. In parallel, machine learning (ML) has been increasingly adopted as a powerful tool for analysing complex ecological systems, where interactions among organisms and their environments are often nonlinear, heterogeneous and scale-dependent. Over the past three decades, ML applications in ecology and global biology have expanded rapidly, supporting advances in pattern detection, prediction, monitoring and process understanding, while still facing major challenges in data quality, sampling bias, interpretability, uncertainty quantification and causal inference. Emerging developments in embodied Artificial Intelligence (AI), foundation models, large language models and intelligent agent systems now create new opportunities for ecological research by enabling adaptive observation, improved integration of heterogeneous data and more efficient analytical workflows. These advances are beginning to steer the field towards an AI-assisted research paradigm, in which ecologists supervise coordinated AI systems for data collection, integration, analysis and modelling, while providing interpretation, validation and iterative feedback. Realizing the full potential of such human-AI synergies in ecological studies will require closer integration between ecological knowledge and advanced AI methods, as well as sustained collaboration among ecologists, data scientists and technology developers.