Machine Learning for Wildlife Monitoring and Conservation: A Review of Methods, Outcomes, and Ethical Challenges

Abstract

Machine learning (ML) has emerged as a transformative technology in wildlife monitoring and conservation, offering new opportunities to improve species identification, population estimation, habitat assessment, and biodiversity protection. This review examines the principal machine learning methods applied in wildlife conservation, their reported outcomes, and the ethical and practical challenges associated with their implementation. It synthesizes research on supervised and unsupervised learning, deep learning, computer vision, acoustic classification, and remote sensing, with particular attention to their applications in camera-trap image analysis, automated species recognition, wildlife population monitoring, movement tracking, poaching detection, and habitat change assessment. The review highlights how these approaches can accelerate the processing of large ecological datasets, reduce the workload associated with manual identification, support near-real-time monitoring, and improve evidence-based conservation planning. However, their effectiveness depends on the availability of representative training data, model accuracy, technological infrastructure, and the ecological contexts in which they are deployed. Key challenges include biased or incomplete datasets, limited generalizability across species and geographical regions, high computational costs, inadequate technical capacity, and difficulties in interpreting model predictions. Ethical concerns also arise from the collection and governance of wildlife data, the potential misuse of precise species-location information by poachers, surveillance-related risks affecting Indigenous and local communities, and unequal access to conservation technologies. The review emphasizes the importance of transparent model development, data protection, community participation, interdisciplinary collaboration, and human oversight. It concludes that machine learning can strengthen wildlife conservation when integrated with ecological expertise, locally informed conservation strategies, and robust ethical safeguards. Future research should prioritize explainable and resource-efficient models, geographically diverse datasets, standardized evaluation frameworks, and equitable technological approaches that support long-term biodiversity conservation.