Applying machine learning to forest soundscapes helps researchers pinpoint rare and threatened birds.
The marbled murrelet is an elusive creature. At sea, the stubby seabird dives at the first sign of predators. On land, it lays its eggs high in the mossy branches of the Pacific Northwest’s old-growth forests—a fact only serendipitously discovered by a utility-company employee climbing trees in the 1970s.
The murrelet is so mysterious that scientists call it the enigma of the Pacific. To try to monitor populations, researchers have donned drysuits to attach radio transmitters to the birds and have flown small airplanes to search for nests—efforts that are expensive and imprecise. Yet despite the scant data, the murrelet is on a clear downhill trajectory. In 1992, it was listed as threatened under the US Endangered Species Act. It suffers from habitat loss caused by logging old-growth forests and from diminishing marine prey due to climate change. Reversing the decline is difficult because the birds are so challenging to study. But by monitoring the soundscape of the forest using artificial intelligence, scientists have a revelatory new approach to studying the protected species.
“The murrelet is often cited as being one of the most difficult forest birds to work on,” says Adam Duarte, a biologist with the US Forest Service and lead author of a new paper on the novel technique. By merging computer science and recording technology with ecology, Duarte says, researchers are able to overcome many of the historical challenges.
To find a more effective way of studying the birds, Matthew Betts, an ecologist at Oregon State University and a coauthor, turned to spotted owl scientists. Like marbled murrelets, spotted owls are threatened and cryptic, and for decades, studying them involved luring the birds to be captured and banded.
read more at hakaimagazine.com.
