Found by an algorithm, not a birdwatcher

Tue, 01 Sep 2026 11:22:55 +1000

No one had seen a plains-wanderer west of Melbourne in thirty years. Thirty-five microphones did it in one season.

The plains-wanderer is critically endangered, standing about 15cm tall, roughly the size of a pencil, with wide yellow eyes that make it look like a cartoon bird. Fewer than 1,000 remain in the wild. It has no living relatives, genetically alone in its own family, which is part of why birdwatchers travel the world hoping to tick it off. It is also spectacularly good at not being seen: it crouches in grass tussocks and relies on camouflage, a habit that keeps it safe from foxes and safe from human eyes too.

So Zoos Victoria stopped trying to see it and started trying to hear it. Working with the Museums Victoria Research Institute and Queensland University of Technology, the team installed 35 audio recorders called song meters across nine properties with suitable grassland habitat west of the city. Left running, the units gathered tens of thousands of hours of sound. A trained call recogniser then sifted through it, “millions of minutes of recordings” according to Museums Victoria’s Dr Karen Rowe, hunting for the female plains-wanderer’s distinctive low “oom”. It threw up the odd false positive (a cow’s moo was one culprit), but human experts confirmed the real ones: the call turned up at two sites, one private and one public, the first confirmed detections in the area since 1989.

Zoos Victoria’s Chris Hartnett called it “like finding gold”. Given that less than 1 per cent of the plains-wanderer’s original grassland habitat still exists in Victoria, it is also proof the species can hang on in landscapes that look, on paper, mostly gone.

The model doing the listening

This kind of work used to require a person with headphones and a lot of patience. What changed is the model doing the sifting.

Google DeepMind’s Perch 2.0 is trained on 1.5 million recordings spanning birds, mammals, amphibians, insects and marine life, 14,795 species in total. Australian species feature heavily, and the paper describing the training process names its central finding the “bittern lesson”, a nod to the Australasian bittern (another notoriously hard-to-hear Australian bird) and to Richard Sutton’s “Bitter Lesson” essay on machine learning. The lesson, in this case, is that a straightforward supervised classifier beats fancier self-supervised approaches once you have enough labelled calls to train on. It now sets the benchmark on two major bioacoustics leaderboards, BirdSet and BEANS.

That is the frontier end. The interesting part is how fast this has moved to the amateur end.

The same idea, running on a security camera

Jason Tucker pointed three existing home security cameras at his backyard and ran their built-in microphone audio through BirdNET-Go, an open-source tool that runs entirely on local hardware. No cloud, no subscription, no API calls out. It identifies birds in real time, flags new species visiting the yard, and pushes alerts to Discord or Home Assistant. Along the way it picked up bats and frogs as well. BirdNET-Go now supports Perch 2.0 as a detection model, recognising those same 14,795 species against the roughly 6,000 the older BirdNET model covered.

The distance between a Zoos Victoria field deployment and a Raspberry Pi wired into a backyard security camera is now mostly a matter of ambition, not access. The same class of model, trained at the same scale, is doing both jobs.

Sources:

Rambling Rows

Found by an algorithm, not a birdwatcher

The model doing the listening

The same idea, running on a security camera