UK Woodland Bat Survey Records 98 Million Calls Using AI
Researchers analyzed 98 million bat calls across 310 British woodlands using AI tools, detecting rare species like the red-listed Barbastelle.
Stock photo for illustration only, not from the actual event
- Researchers surveyed 310 woodland sites across Britain over a three-year period
- AI tools processed 98 million bat calls from 90,000 hours of audio recordings
- Rare Barbastelle bats were detected in south-west England including Somerset and Devon
The largest-ever woodland bat survey in Britain has concluded after researchers deployed recording devices on poles across 310 forest locations. Over a span of three years, the equipment captured a staggering 98 million bat calls, accumulating approximately 90,000 hours of acoustic data.
Without the implementation of artificial intelligence, project leaders estimate that manually analyzing this massive volume of recordings would have required more than 30 years. Efficient open-source AI tools enabled the team to successfully identify millions of calls across thousands of nights, spot behavioral patterns, and map species distribution nationwide.
The analysis revealed the presence of some of Britain's rarest and most threatened bat species, including the red-listed Barbastelle. Known for roaming ancient forests, the Barbastelle has historically proven exceptionally difficult to track due to its elusive nature.
Hotspots of activity for the Barbastelle were found to be concentrated across the south-west of England. Lia Gilmour of the Bat Conservation Trust noted that counties such as Somerset, Devon, Cornwall, and Dorset have emerged as strongholds for these vulnerable mammals.
Stock photo for illustration only, not from the actual event
In biodiversity monitoring, we view AI as a powerful tool. We were looking at huge volumes of data and 90,000 hours of recording, there was no way we would be able to do this manually.
Lia Gilmour, Bat Conservation Trust
This initiative highlights the growing impact of artificial intelligence in environmental conservation, often referred to as AI for Good. Automated bioacoustic monitoring utilizes machine learning models trained on specific animal vocalization frequencies to classify wildlife sounds accurately at scale, transforming how researchers study ecological health and animal populations without disturbing natural habitats.
Conducted alongside Forest Research, the unprecedented survey demonstrates how advanced technology can accelerate ecological research and provide critical insights needed for effective wildlife conservation strategies in the UK.
Source: BBC Science & Environment
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