Artificial intelligence-driven solutions for mitigating human-wildlife conflict in biodiversity hotspots

Area of Science:

• Conservation Science
• Artificial Intelligence
• Biodiversity Studies

Background:

• Biodiversity hotspots face significant threats from human-wildlife conflict (HWC), impacting both conservation and human development.
• HWC challenges wildlife protection and community livelihoods, necessitating innovative solutions.

Purpose of the Study:

• To review and analyze the application of artificial intelligence (AI) in mitigating human-wildlife conflict.
• To assess AI's effectiveness in HWC monitoring, prediction, and decision support.

Main Methods:

• Systematic literature review of 105 studies (1990-2025) from 163 sources.
• Analysis of AI-driven technologies including machine learning, deep learning, computer vision, remote sensing, and GIS.
• Examination of integrated platforms and participatory data approaches.

Main Results:

• AI significantly improved HWC monitoring (65%), predictive accuracy (47%), and community engagement (39%).
• AI technologies facilitate large-scale data processing, automated species identification, and real-time decision-making.
• Integrated platforms and spatial tools enhance situational awareness and strategic conservation planning.

Conclusions:

• AI advancements are transforming HWC surveillance, enabling proactive and sustainable biodiversity conservation.
• Integrating AI with local knowledge and participatory governance is crucial for equitable conservation outcomes.
• Further research and policy development are needed to maximize AI's potential in addressing HWC.