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Why Mexican bat calls are unlocking the secrets of its ecosystem

Some bat species, such as this greater mouse-eared bat, are hard to identify by calls Some bat species, such as this greater mouse-eared bat, are hard to identify by calls Martin Janca/Shutterstock
21 Apr
2016
The world’s biggest library of bat sounds has been compiled in order to monitor some of the most biodiverse regions of Mexico

Often portrayed as cacophonies of screeching and chirping, colonies of bats can be an ominous sight to see and hear. However, the noises of bats can tell us a lot about an ecosystem. In Mexico, a country with one of the highest number of bat species and some of the highest rates of species extinction and habitat loss, a new library of thousands of bat calls is helping scientists monitor such ecosystems.

‘Audio surveys are increasingly used to monitor biodiversity change,’ says Dr Veronica Zamora-Gutierrez, a zoologist at University College London (UCL) and lead curator of the library and author of the corresponding study. ‘Bats are especially useful for this as they are an important indicator species, contributing significantly to ecosystems as pollinators, seed dispersers and suppressors of insect populations.’ Venturing into some of the most biodiverse regions of Mexico, a team of researchers from UCL, the Zoological Society of London (ZSL) and the University of Cambridge collected 4,685 calls from 1,378 individual bats. Together these calls represent 59 of an estimated 130 bat species found in the country.

Identifying the sounds of bats communities in a given area can give scientists a benchmark for the ecosystem. In the long-term, monitoring the acoustics of the same sights can show the impacts of rapid environmental change. ‘It exploits the bats’ echolocation behaviour,’ says co-author Brock Fenton, biologist from Western University, ‘letting them tell us who’s where and when.’

However, collecting bat calls is not as simple as it sounds. ‘The challenge is that they sound so similar,’ says Fenton. ‘We overcame this by using machine learning algorithms together with information about hierarchies to automatically identify different bat species,’ explains Zamora-Gutierrez. So far, the algorithms have proved to be 72 per cent accurate at a species level and 91.7 per cent accurate at a family level. ‘The tool only has great potential so long as local researchers make efforts to confirm that the identifications are accurate,’ warns Fenton, ‘so it’s not a silver bullet, but it’s a good start.’

Co-author, Professor Kate Jones of UCL and ZSL, said: ‘We’ve shown it is possible to reliably and rapidly identify bats in mega-diverse areas – such as Mexico – and we hope this encourages uptake of this method to monitor biodiversity changes in other biodiversity hotspot areas such as South America.’

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