Artificial Intelligence in Wildlife Tracking and Conservation

Abstract

The technology of artificial intelligence (AI) has transformed the wildlife tracking and conservation process altogether since now, the tracking can be more precise and the analysis can be predictive and data-driven. As the biodiversity declines due to habitat loss, climate change, poaching and human-wildlife conflict, conservation decisions need to be supported with scalable and specific technological assistance. The present paper explains how AI-based applications, including machine learning algorithms, computer vision, bioacoustic monitoring, and satellite-based analytics, are enhancing wildlife research and protection processes. Application to AI, such as camera trap surveillance, drone-assisted surveillance, and interpretation of GPS collar data, can significantly decrease the number of individuals that ought to be employed to undertake this task and also increase the detection rates and behavioral cues. Predictive modeling and how it has been used in predicting the migration pattern and the high-risk poaching areas and assessing the suitability of the habitat in the different environmental conditions is also addressed in the paper. The AI systems can enable real-time monitoring and response plans through the union of the big data of the remote sensing, environmental sensors, and ecological records. The paper will also discuss the ethical, technical and logistical challenges regarding the application of AI in conservation, including information bias, technological availability, the inability to access infrastructures in remote locations and misuse of surveillance risks. This paper makes an argument on the foundation of an overview of new case studies and interdisciplinary findings that AI does not displace ecological knowledge but enhances conservation success when coupled with field knowledge and community engagement. The findings suggest the opportunities of AI to facilitate the procedure of resources allocation, enhance the species protection, and aid in evidence-based policymaking. Finally, one of the potential avenues of how more adaptive, efficient and sustainable management of biodiversity can be realized is the application of artificial intelligence in wildlife conservation equipment in the environment of a rapidly changing environmental change.