Leveraging Data Analytics for Sustainable Resource Management in Wildlife Conservation

Vishal Shah

Abstract: Utilizing data analytics, Internet of Things technology, and machine learning algorithms, this study offers a novel framework for wildlife conservation that tackles the increasing difficulties of species protection and habitat preservation. The suggested system uses a three-tier architecture that combines edge computing and cloud-based AI processing for real-time analysis with thermal imaging, acoustic monitoring, and environmental sensors for thorough data collection. The framework makes it possible to precisely track individual animals, analyze behavior patterns, and evaluate the health of ecosystems by utilizing biometric recognition algorithms and automated attribution processes. The system’s advanced threat detection mechanisms and modular design that guarantees scalability across various ecosystems and adaptive learning capabilities through ensemble techniques are some of its unique features. Conservation professionals can access long-term datasets, personalized reports, and real-time insights for evidence-based decision-making via the Movebank system and dedicated web portal. By combining traditional conservation techniques, this integrated approach creates a proactive data-driven solution that supports sustainable resource management techniques and strengthens wildlife protection initiatives.