How AI is Revolutionizing Endangered Species Conservation

By Sunil Sonkar
2 Min Read
How AI is Revolutionizing Endangered Species Conservation

AI is revolutionizing how we protect endangered species. Researchers worldwide are increasingly relying on the new and evolving technology to track and safeguard biodiversity. The outcomes are indeed encouraging. In contrast to traditional approaches that is often marked by disruption and high resource demands. AI presents a faster and more streamlined means to scrutinize extensive real-world data.


AI is not a magic bullet. Nicolas Miailhe, founder of The Future Society, emphasized the need for human involvement in designing models, data collection, labeling, quality checks and data interpretation. However, AI can accelerate vital discoveries when combined with human expertise.

Researchers are already proving the worth of AI in biodiversity preservation. In Germany, ecologists demonstrated how AI can count different animal species in tropical forests by listening to audio recordings. By analyzing animal ‘soundscapes,’ AI can assist experts in identifying various species accurately.

In addition to identifying species, AI is being used to monitor wildlife through drones and camera traps. Organizations like Conservation AI have developed models capable of automatically analyzing images, videos and audio files. These AI-powered platforms have processed millions of images and have identified individual animals across multiple species including critically endangered ones. The speed at which AI processes data is an advantage for conservationists.

AI is not just about real-time monitoring. It can also help model the impact of human activities on ecosystems and reconstruct historical changes. A recent study showed how AI can connect past environmental changes to the decline in biodiversity by studying genetic material in lake sediment layers. The findings shed light on the environmental factors contributing to biodiversity decline.

The key benefit of AI in conservation is its data-driven approach. AI learns from historical data and predicts future trends in biodiversity more accurately than ever before. It offers a hypothesis-free way to understand the complexities of ecological systems.

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