A review of data science in urban bird ecology: from qualitative description to quantitative governance

This review examines how remote sensing, sensor networks, citizen science, machine learning, and ecological modeling are reshaping research on birds and urban ecological environments. It synthesizes recent advances in automated bird recognition, multi-source data integration, species distribution modeling, and ecological corridor assessment, with emphasis on key data sources, feature engineering strategies, and modeling frameworks. Existing studies show that these approaches improve the monitoring of urban bird diversity, strengthen habitat assessment, and support bird-friendly planning, ecological risk identification, and conservation management. The review also identifies major limitations, including weak causal inference, scale mismatches among datasets, limited model interpretability, and insufficient translation of research outputs into planning practice. Future work should promote interdisciplinary collaboration and strengthen the linkage between quantitative analysis and governance-oriented applications.