News Release

Researchers predict population trends of birds worldwide

Peer-Reviewed Publication


In a study published in Ibis, investigators combined the power of big data and machine learning, a type of artificial intelligence, to predict population declines for bird species with unknown population trends and used correlation analyses to identify predictors of bird population declines worldwide. 
After training and testing their machine learning model on data from 10,163 species with known population trends, the researchers estimated that nearly half (47%) of the 801 bird species with currently unknown population trends are declining.  
Correlation analyses suggested that globally, the top predictor associated with bird population declines was a severely fragmented population, with non-migratory birds in South American and Southeast Asian tropical and subtropical forests being particularly vulnerable. 

“I see endless possibilities for conservation biology when artificial intelligence is brought into the picture, and we are still not exploring enough,” said lead author Xuan Zhang, of Bird Ecology and Conservation Ontario.

Additional Information 

Link to Study:

About Journal

IBIS publishes original papers, reviews and short communications reflecting the forefront of the research activity in ornithological science, but with special emphasis on the conservation, ecology, ethology and systematics of birds.

About Wiley 

Wiley is a global leader in research and education, unlocking human potential by enabling discovery, powering education, and shaping workforces. For over 200 years, Wiley has fueled the world’s knowledge ecosystem. Today, our high-impact content, platforms, and services help researchers, learners, institutions, and corporations achieve their goals in an ever-changing world. Visit us at, like us on Facebook and follow us on Twitter and LinkedIn

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.