Wildlife Tracking and Habitat Conservation with AI

Wildlife conservation increasingly relies on advanced technologies to address challenges posed by habitat loss, climate change, and human–wildlife conflict. Traditional wildlife tracking methods such as GPS collars, VHF telemetry, and camera traps have provided valuable insights into animal movement and behavior, but they often generate large volumes of data that are difficult and time-consuming to analyze manually. Artificial Intelligence (AI), particularly machine learning and computer vision, has emerged as a powerful tool to enhance wildlife tracking, monitoring, and habitat conservation. This paper explores the application of AI-driven technologies in wildlife tracking, focusing on GPS-based tracking integrated with machine learning, VHF telemetry, AI-enabled camera traps, and remote sensing for habitat monitoring. A detailed case study using the YOLO11 object detection model trained on an African wildlife dataset demonstrates the effectiveness of deep learning in accurately detecting and classifying animal species such as buffalo, elephant, rhino, and zebra. The trained model achieved high precision, recall, and mean Average Precision (mAP) values, highlighting its suitability for real-time wildlife monitoring. Additionally, the paper discusses the role of AI in habitat connectivity analysis using tools such as Circuitscape, which supports the design of wildlife corridors and conservation planning. Despite its potential, the use of AI in wildlife conservation faces challenges related to data quality, ethical considerations, and technical limitations. The paper concludes by outlining future prospects, including citizen science integration, ecosystem restoration, and collaborative AI platforms, emphasizing AI’s transformative role in improving wildlife conservation strategies.