AI for Sustainable Wildlife Conservation: Predicting Species Decline Using Machine Learning

Abstract

The rapid decline of biodiversity due to habitat loss and climate change necessitates data-driven conservation strategies. This paper explores machine learning applications in wildlife conservation, including species population prediction, habitat monitoring, and poaching detection. Computer vision techniques using satellite imagery, acoustic monitoring, and predictive analytics are employed to assess biodiversity health and forecast species decline. Case studies on AI-driven conservation projects demonstrate the effectiveness of machine learning in mitigating human impact on ecosystems and ensuring long-term sustainability in wildlife preservation efforts.