Using artificial intelligence to track birds' dark-of-night migrations

In a first, UMass Amherst, Cornell use AI to mine big migration data on massive scale

Date: August 28, 2019

Source: University of Massachusetts at Amherst

On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services' network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers.

That's because the sheer magnitude of information and lack of tools to analyze it made only limited studies possible, says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst.

Ornithologists and ecologists with the time and expertise to analyze individual radar images could clearly see patterns that allowed them to discriminate precipitation from birds and study migration, he adds. But the massive amount of information ¬- over 200 million images and hundreds of terabytes of data -- significantly limited their ability to sample enough nights, over enough years and in enough locations to be useful in characterizing, let alone tracking, seasonal, continent-wide migrations, he explains.

Clearly, a machine learning system was needed, Sheldon notes, "to remove the rain and keep the birds."

Now, with colleagues from the Cornell Lab of Ornithology and others, senior authors Sheldon and Subhransu Maji and lead author Tsung-Yu Lin at UMass's College of Information and Computer Sciences unveil their new tool "MistNet." In Sheldon's words, it's the "latest and greatest in machine learning" to extract bird data from the radar record and to take advantage of the treasure trove of bird migration information in the decades-long radar data archives. The tool's name refers to the fine, almost invisible, "mist nets" that ornithologists use to capture migratory songbirds.

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Weather forecasting sheds light on where and when birds will fly

Date:  September 13, 2018

Source:  University of Oxford

Using a combination of AI and weather forecasting can help scientists to predict the movements of millions of birds and support their conservation goals, according to new Oxford University research.

Conducted in collaboration with Cornell University, the study -- published in the journal Science -- reports that scientists can now reliably predict these waves of bird migration across the United States, up to seven days in advance. It reveals the underlying methods that power migration forecasts, which can be used as a bird conservation tool.

September is the peak of autumn bird migration, and billions of birds are winging their way south in dramatic pulses. In this study, the researchers reviewed 23 years of spring bird migration across the United States using 143 weather radars, highly sensitive sensors that scientists can use to monitor bird movements. They filtered out precipitation and trained a machine learning model to associate atmospheric conditions with levels of bird migration countrywide. Eighty percent of variation in bird migration intensity was explained by the model.

Benjamin Van Doren, a doctoral student at the University of Oxford and a Cornell University graduate, said: "Most of our songbirds migrate at night, and they pay close attention to the weather. Our model converts weather forecasts into bird migration forecasts for the continental United States."

Continued

The sounds of climate change

AI to analyze field recordings and estimate songbird arrivals

Date:  June 20, 2018

Source:  Lamont-Doherty Earth Observatory, Columbia University

Summary:

Researchers describe a way to quickly sift through thousands of hours of field recordings to estimate when songbirds arrive at their Arctic breeding grounds. Their research could be applied to any dataset of animal vocalizations to understand how migratory animals are responding to climate change.

Spring is coming earlier to parts of the Arctic, and so are some migratory birds. But researchers have yet to get a clear picture of how climate change is transforming tundra life. That's starting to change as automated tools for tracking birds and other animals in remote places come online, giving researchers an earful of clues about how wildlife is adapting to hotter temperatures and more erratic weather.

In a new study in Science Advances, researchers at Columbia University describe a way to quickly sift through thousands of hours of field recordings to estimate when songbirds reached their breeding grounds on Alaska's North Slope. They trained an algorithm on a subset of the data to pick out bird song from wind, trucks and other noise, and estimate, from the amount of time the birds spent singing and calling each day, when they had arrived en masse.

The researchers also turned the algorithm loose on their data with no training to see if it could pick out bird songs on its own and approximate an arrival date. In both cases, the computer's estimates closely matched what human observers had noted in the field. Their unsupervised machine learning method could potentially be extended to any dataset of animal vocalizations.

"Our methods could be retooled to detect the arrival of birds and other vocal animals in highly seasonal habitats," said the study's lead author, Ruth Oliver, a graduate student at Columbia. "This could allow us to track largescale changes in how animals are responding to climate change."

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