Somewhere in Puerto Rico, a small yellow frog is chirping into a microphone attached to an iPod. Several kilometers away, a computer is listening. Within a minute, that song will be posted online, and the species of the frog will be identified — all without scientists lifting a finger.
This wildlife recording studio is part of a new project to study biodiversity using automated hardware and software. ARBIMON, which stands for automated remote biodiversity monitoring network, was developed by Mitchell Aide and Carlos Corrada-Bravo from the University of Puerto Rico, who report their new work this week in the journal PeerJ. They teamed up to apply 21st century technology to the problem of species monitoring, combining readily available parts with advanced machine-learning algorithms to analyze thousands of hours of wildlife audio in real time.
Scientists have long used automated technology to track deforestation, but they haven’t had nearly as much success in developing similar techniques to monitor the effects of climate change and habitat loss on fauna. “We don’t have good, long-term data on how these pressures are affecting the abundance or distribution of species,” says Aide. The challenge is that human researchers can only be in so many places at once, and only for so long. And even when they deploy automated recorders, thousands of skilled man-hours are required to sift through the resulting data.
That’s where ARBIMON’s new software comes in handy.