TLDR:
Key Points:
- Oregon researchers are using AI to study threatened coastal seabirds, specifically the marbled murrelet.
- Researchers developed a machine learning algorithm to analyze millions of hours of audio data to identify call patterns of the marbled murrelets.
Key Elements of the Article:
Oregon researchers are utilizing artificial intelligence tools to study hard-to-reach threatened species such as the marbled murrelet. The researchers collected extensive audio data from federal forests in Washington and Oregon between 2018 and 2022, which would be impossible to manually review without computational tools.
The development of a machine learning algorithm by researchers, led by OSU College of Forestry doctoral student Matthew Weldy, has enabled the identification of call patterns of marbled murrelets. This breakthrough, published in the Ecological Indicators journal, has the potential to enhance habitat conservation efforts by understanding the crucial regions for these seabirds.
Marbled murrelets are listed as threatened under the U.S. Endangered Species Act, necessitating protection from harmful human activities like logging. Traditional survey methods for murrelets have been labor-intensive and costly, involving physically capturing the birds, attaching radio transmitters, and tracking them to their nests.
The use of AI technology allows researchers to attach audio recorders to trees in forests, collecting data on the birds’ natural calling patterns without disruptive human interventions. By repurposing existing recorders used for monitoring northern spotted owls, researchers can efficiently study multiple species in a single monitoring effort.
The research team, led by Matthew Betts, plans to expand this AI monitoring approach to study approximately 140 other species, marking a new era of data collection for ecological research. This method offers a more cost-effective and less intrusive way to study and protect threatened wildlife populations.