̽��ֱ��

An international team led by scientists from the ̽��ֱ�� of Cambridge, ̽��ֱ�� College London (UCL), and the Zoological Society of London (ZSL), developed the reference call library and a new way of classifying calls to accurately and quickly identify and differentiate bat species.

̽��ֱ��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.

It is the first time automatic classification for bat calls has been attempted for a large variety of species, most of them previously noted as hard to identify acoustically.

“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,” explains lead author Dr Veronica Zamora-Gutierrez, from the ̽��ֱ�� of Cambridge Conservation Research Institute and UCL.

“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.”

“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 ��of the over 130 species occurring in Mexico.

Most of the areas hadn’t been sampled before and the data collected, along with additional information from collaborators, provides calls for over half of the species and all of the families of bats in Mexico.

Co-author, Professor Kate Jones, 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.”

“Our ability to readily map ecological communities is imperative for understanding the impact of the Anthropocene and implementing effective conservation measures.”

̽��ֱ��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 website which will allow them to refine the machine learning algorithms used by the software.

̽��ֱ��study also involved researchers from the IPN CIIDIR Durango (Mexico), Universidad Veracruzana (Mexico), Western ̽��ֱ�� (Canada), ̽��ֱ�� of Bristol, ̽��ֱ�� of Ulm (Germany), Smithsonian Tropical Research Institute (Panama), Ernst-Moritz-Arndt ̽��ֱ�� (Germany), ̽��ֱ�� College Dublin and ̽��ֱ�� of Warwick. It was kindly funded by CONACYT, Cambridge Commonwealth European and International Trust, ̽��ֱ��Rufford Foundation, American Society of Mammalogists, Bat Conservation International, Idea Wild, ̽��ֱ��Whitmore Trust and Engineering and Physical Sciences Research Council (EPSRC).

Adapted from a ̽��ֱ�� College London press release.

Inset image:�� ̽��ֱ��western yellow bat (Lasiurus xanthinus) is a species of vesper bat found in Mexico and the south-western United States (UCL/ZSL).