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.

The key was to remove a bit of the human element and replace it with computers. “The main contribution has been the software side of it,” Aide explained. “Lots of people are wandering around with external hard drives full of recordings and have no way of analyzing them or managing them.”

The heart of the ARBIMON recording unit is nothing more than an inexpensive microphone attached to an iPod. Wired to an antenna that can transmit the data to a base station as far as 40 kilometers away, the whole setup is powered by a solar panel and a car battery, tucked away from the elements inside a waterproof case. From that base station, the data is sent over the internet to Puerto Rico, where ARBIMON’s servers go to work.

Listen to a plains coquí (E. juanariveroi) below, recorded on an ARBIMON unit:

In under a minute, machine-learning algorithms have analyzed the audio files, scanning the frequencies for patterns indicative of a specific species. So far the team has used the technology to single out calls from several frogs, a couple birds, a monkey, and two yet-to-be identified insects.

The plains coquí is an endangered Puerto Rican frog named for its unique “co-kee” call. The ARBIMON software has been fed a little bit of data about its characteristic sound pattern, and any time the small, yellow toad’s call is registered by the recorder, it is automatically picked out and cataloged. In their new paper, Aide and his colleagues listened to the plains coquí for five years. The audio analysis showed that the frog calls declined for four straight years, and recovered in the fifth.

The software isn’t perfect at picking out calls, especially in the midst of background noise like rain or urban clamor. Furthermore, it can only be used to catalog species that make noise, and only within range of the microphone.

Aide hopes that national parks and conservation groups will join in to deploy their $5,000 units across the globe. “I like to think of these as biodiversity weather stations,” Aide says of the listening posts. He envisions scientists uploading all those untouched hours of audio to the ARBIMON servers in order to finally catalog what graduate students never could, creating volumes of audible museum specimens.

Guests can access these species recordings on the ARBIMON Acoustics website.