Latest International Research: AI Assisted Animal Sound Landscape Can Effectively Monitor Forest Biodiversity Restoration

Tech 2023-10-19 05:16:27 Source: Network

Beijing, October 18 (Reporter Sun Zifa) - A biodiversity research paper published by Springer Nature's academic journal "Nature Communications" suggests that artificial intelligence (AI) assisted animal soundscape can be used as an effective tool for monitoring forest biodiversity restoration after farmland abandonment. This research findings propose an automated, cost-effective, and reliable method for detecting forest biodiversity and evaluating restoration outcomes

Beijing, October 18 (Reporter Sun Zifa) - A biodiversity research paper published by Springer Nature's academic journal "Nature Communications" suggests that artificial intelligence (AI) assisted animal soundscape can be used as an effective tool for monitoring forest biodiversity restoration after farmland abandonment. This research findings propose an automated, cost-effective, and reliable method for detecting forest biodiversity and evaluating restoration outcomes.

This paper introduces that large-scale monitoring of forest biodiversity is important for conservation, but requires cost-effective standardized tools. Previous studies have shown that bioacoustics (the discipline of studying animal sounds) is a promising tool for detecting animal communities using soundscapes, but there is still significant uncertainty in this regard, such as whether these soundscapes can also reflect the state of non vocal animal species. This not only requires the combination of traditional acoustic measurement and machine learning methods, but also faces technical difficulties.

In the process of this study, the authors also evaluated their sound-based results using a different type of ecological information. They used insect diversity data obtained from DNA macro barcodes, mainly non vocal species. The research results indicate that although these two databases may not fully match, combining bioacoustics and deep learning has the potential to monitor forest biodiversity. (End)

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