By Lola Gayle, Editor-at-large
Nothing lost in translation here! Researchers have compiled a library of bat sounds to help identify bats from their calls in Mexico, a country which harbors many of the Earth’s species and has one of the highest rates of extinction and habitat loss.
The international team behind the library was led by University College London (UCL), University of Cambridge, and the Zoological Society of London (ZSL). The researchers also developed a new way of classifying calls to quickly and accurately identify and differentiate between bat species.
According to a statement from UCL, it is the first time automatic classification for bat calls has been attempted for such a large variety of species, most of which are notorious for being hard to identify acoustically.
While this method was developed for monitoring bat species distribution in remote and understudied regions in Mexico, the researchers say it could 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,” explained lead author Dr. Veronica Zamora-Gutierrez, from UCL and the University of Cambridge. “By tracking the sounds they use to explore their surroundings, we can characterize the bat communities in different regions in the long term and gauge the impact of rapid environmental change.”
“Before now it was tricky to do as many bat species have very similar calls and differ in how well they can be detected,” Dr. Zamora-Gutierrez continued. “We overcame this by using machine learning algorithms together with information about hierarchies to automatically identify different bat species.”
For their study, 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 representing 59 of the ~130 species found in Mexico.
Most of the areas the researchers focused on hadn’t previously been sampled. The data collected, along with additional information from collaborators, provided calls for 69% of species, 79% of genera and 100% of the families of bats in Mexico. The classifier covered 43% of the species, 51% of the genera, and 100% of the families.
The team tested the accuracy of the call classifier at grouping bats and found it to be 72% accurate at species level, 91.7% accurate at family level, 77.8% accurate at genus level, 82.5% accurate at guild level – a species grouping reflecting the bats’ preferences for foraging habitat and food. It is the first time guilds have successfully been used instead of species to identify functional groups.
“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,” added Co-author, Professor Kate Jones, UCL and ZSL. “Our ability to readily map ecological communities is imperative for understanding the impact of the Anthropocene and implementing effective conservation measures.”
The team now plans to develop a citizen science monitoring program for Mexican bats in order to collect further information on bat calls. They also plan to develop more robust tools for bat identification using the Bat Detective website. This will allow them to refine the machine learning algorithms used by the software, Caygill wrote.
Results of this study are published in the journal Methods in Ecology and Evolution.
Top Image: The pallid bat (Antrozous pallidus) is a species of bat that ranges from western Canada to central Mexico. Connor Long/Wikimedia (CC BY-SA 3.0)