AI & Environment Resource HubReynolds et al. · 2025-02-01
A horizon scan with conservation scientists and AI experts identifies 21 impactful AI applications for biodiversity conservation, including digital twins and AI-powered advisors, emphasizing benefits while acknowledging the need for equitable and complementary integration with traditional methods.
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Reynolds et al.. 2025-02-01. The Potential for AI to Revolutionize Conservation: A Horizon Scan. https://www.cell.com/trends/ecology-evolution/fulltext/S0169-5347(24)00286-6?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0169534724002866%3Fshowall%3Dtrue (AI & Environment Resource Hub; record paper-046; collection snapshot 2026-10-04).
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Combined source rows: Scientific Paper 50; Scientific Paper 207.
Source sheet: Scientific Paper · Row 50 · Original ID: paper-046.
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Shared topics: Biodiversity and ecosystems
Improved machine learning techniques enhance bird call detection in passive acoustic monitoring, boosting precision and biodiversity tracking in climate research.
Shared topics: Biodiversity and ecosystems
This study proposes a low-cost IoT-based TinyML model that intelligently activates mosquito repellent only when an Anopheles mosquito is detected, using a 1D-CNN for efficient classification of mosquito wingbeat sounds, reducing energy waste and potential health risks.
Shared topics: Biodiversity and ecosystems
A hybrid machine learning model improves soil respiration predictions, enhancing carbon cycle modeling and greenhouse gas management strategies.
Shared topics: Biodiversity and ecosystems
A deep learning-based bioacoustic AI tool enables citizen scientists and law enforcement to accurately identify traded songbird species in Asian markets, achieving 91.6% accuracy and supporting conservation efforts against overexploitation and illegal wildlife trade.