The team, led by researchers from UCL, the University of Cambridge and the Zoological Society of London, conducted the study in Mexico, a country which harbours many of the Earth’s species and has one of the highest rates of species extinction and habitat loss.

This is the first time automatic classification for bat calls has been attempted for a large variety of species.

The researchers say the method can be used to monitor biodiversity change and complete information on bat species distributions in remote and understudied regions in Mexico. It could also be expanded for use in other areas across the Neotropics, which incorporates South and Central America, and the Caribbean Islands and Florida.

“Audio surveys are increasingly used to monitor biodiversity change, and 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.  By tracking the sounds they use to explore their surroundings, we can characterise the bat communities in different regions in the long term and gauge the impact of rapid environmental change,” said lead author Dr Veronica Zamora-Gutierrez from UCL and the University of Cambridge. 

“Before now it was tricky to do as many bat species have very similar calls and differ in how well they can be detected.  We overcame this by using machine learning algorithms together with information about hierarchies to automatically identify different bat species.”

For the study, published today in Methods in Ecology and Evolution, the researchers ventured into some of the most dangerous areas of Mexico, primarily the northern deserts, to collect 4,685 calls from 1,378 individual bats from 59 different species.

The team now plan on developing a citizen science monitoring programme for Mexican bats to collect further information on bat calls. They will also develop more robust tools for bat identification using the Bat Detective website which will allow them to refine the machine learning algorithms used by the software.

Paper

‘Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design’ by Veronica Zamora-Gutierrez, Celia Lopez-Gonzalez, M. Cristina MacSwiney Gonzalez, Brock Fenton, Gareth Jones, Elisabeth K.V. Kalko, Sébastian J. Puechmaille, Vassilios Stathopoulos and Kate E. Jones in Methods in Ecology and Evolution