AI in the World of Ornithology: Using AI to Learn More About Birds
Estimated Reading Time: 5 minutes
- Artificial Intelligence, particularly projects like BirdCast, is revolutionizing ornithology by using machine learning to predict bird migration based on weather data.
- Understanding bird migration is crucial for assessing ecosystem health, fostering human connection with nature, and advancing fundamental scientific knowledge.
- Machine learning is indispensable for analyzing vast radar data, accurately distinguishing bird movements from meteorological events, and powering highly precise migration forecasts.
- The human element, through citizen science, remains vital by providing foundational “ground truthing” data and engaging the public in data collection for AI models.
- AI-driven insights directly contribute to bird conservation efforts, enabling proactive measures such as advising cities to dim lights during peak migration to prevent collisions.
- Why Bird Migration Matters: Insights from a Pioneer
- Deciphering the Skies: How AI Powers BirdCast
- The Human Element: Citizen Science in an AI World
- Conclusion
- Frequently Asked Questions
Hi everyone, Malena here—I’m an investigative reporter at The Markup, but I’m taking a break from my usual deep diving to talk about something a little more airy and light: birds! More specifically, I’ve become interested in how artificial intelligence (AI) is reshaping the world of ornithology. One of the most substantial ways is through BirdCast, a project that uses machine learning to help scientists predict how birds migrate based on weather data. BirdCast is a collaboration between the Cornell Lab of Ornithology, Colorado State University, and UMass Amherst. Launched in 2018, it uses data collected from 143 radar sites across the lower 48 U.S. states to identify bird movements and forecast how those birds will migrate.
This fascinating intersection of cutting-edge technology and natural history is spearheaded by experts like Andrew Farnsworth, a visiting scientist at the Cornell Lab of Ornithology. Farnsworth has been involved with the project since its inception in the 1990s and played a crucial role in securing the grant for BirdCast’s current iteration. His journey into birding, sparked by a scientist grandfather and a green suburban environment, led to a career dedicated to understanding and protecting avian life.
Why Bird Migration Matters: Insights from a Pioneer
Understanding the intricate patterns of bird migration is more than just academic curiosity; it’s fundamental to ecological health and human connection with nature. Andrew Farnsworth highlights three core reasons for its importance:
“At some fundamental level, birds are really good indicators of their environment and the health of their environment. Because of that, studying migratory birds in particular, because they connect so many parts of the planet, is just a very good way of starting to quantify and qualify, how are our ecosystems? How do we value them? How do we evaluate them? How healthy are they?”
Beyond environmental health, Farnsworth notes that birds are “a very good way to engage people.” Their observable movements and diverse species provide an accessible entry point for public interest in science and nature. Lastly, there’s the pursuit of basic science itself. Quantifying phenomena like the number of birds migrating at night over the U.S.—a figure only established in 2018—provides crucial foundational knowledge that technology is now making possible.
Deciphering the Skies: How AI Powers BirdCast
The core challenge for BirdCast lies in sifting through vast amounts of radar data to discern bird movements from meteorological events. This is where machine learning becomes indispensable. Andrew Farnsworth explains:
“We need to, from the radar data, extract what’s weather and what’s biology or birds. Labeling that and turning it into a model that then can very quickly assign, ‘oh, we’re talking only about birds in these data,’ machine learning is critical for that… The heaviest lift for machine learning in the project relates to that creation of the datasets, talking about bird migration as opposed to precipitation or other atmospheric phenomena.”
The algorithms are trained to recognize distinct patterns. Irregular shapes and highly variable reflectivity values typically indicate weather phenomena like thunderstorms. In contrast, migrating birds usually present as more uniform, consistent patterns on the radar. While distinguishing large numbers of fast-flying insects from birds can still pose a challenge, the models are constantly improving.
The accuracy of BirdCast’s AI models is impressive. The system excels at identifying birds versus non-bird phenomena, with correctness rates in the “way high 90s.” The forecast model, which predicts migration intensity based on weather variables, achieves about “80 percent accuracy.” As Farnsworth clarifies, “It’s not perfect, as 80 percent is not 100 percent. But it is by far more than good enough to do a great job of predicting bird migration intensity across the U.S. where we’ve employed this, and to do so consistently across the years.”
Looking ahead, AI’s role in ornithology is set to expand even further. It will increasingly integrate “citizen science or community science” observations and analyses of “flight calls and vocalizations of birds migrating at night.” This convergence of radar, visual, and acoustic data, all processed by advanced machine learning, promises an even more comprehensive understanding of avian life.
The Human Element: Citizen Science in an AI World
While AI brings unprecedented computational power, the human element remains irreplaceable. Ornithology is unique in its reliance on contributions from “regular people.” Andrew Farnsworth stresses that humans are the “experts” who provide the foundational data for AI models:
“All the things you do in your brain that gets you from, ‘Oh, I saw a robin, and I saw 10 of them, and they were at this particular spot.’ That’s essential information for these models going forward. And so the contributions become the raw data… the machine learning operates on.”
These human observations, whether from seasoned birders or curious novices, serve as critical “ground truthing” for the aerial data collected by radar. The information generated by people acts as the “kernels” that allow AI models to learn, refine their predictions, and ultimately, paint a more accurate picture of bird populations and movements.
More broadly, AI is transforming how we engage with birds daily. Every time a person uses a smartphone app for bird identification, they are directly interacting with machine learning. This technology empowers countless individuals to connect with the avian world, making sophisticated identification accessible and broadening the base of observational data vital for future research.
Actionable Steps: How You Can Contribute to Bird Science
The integration of AI into ornithology creates exciting new avenues for public participation. Here’s how you can make a meaningful difference:
- Become a Citizen Scientist: Join online platforms like eBird or iNaturalist to log your bird sightings and sounds. Your observations contribute directly to massive datasets that scientists and AI models rely on for analysis and training.
- Utilize AI Bird ID Apps: Download and use apps like Merlin Bird ID. These tools not only enhance your personal birding experience by helping with identification but also gather valuable data that can further train and improve machine learning algorithms.
- Support Ornithological Research: Consider donating to or volunteering with leading research institutions such as the Cornell Lab of Ornithology. Your support enables the continued development of cutting-edge AI tools and critical conservation projects.
Real-World Impact: Protecting Birds on the Move
The practical benefits of AI in ornithology are tangible and directly contribute to conservation. BirdCast’s accurate migration forecasts, for instance, are invaluable for informing initiatives to protect migratory birds. Knowing when and where significant numbers of birds will be in transit allows cities to implement strategies like dimming unnecessary lights during peak migration nights, drastically reducing collisions with buildings. This proactive, data-driven approach, powered by advanced machine learning, translates directly into saving countless bird lives.
Conclusion
The fusion of artificial intelligence and ornithology marks a monumental leap in our ability to understand and protect avian species. Projects like BirdCast, driven by machine learning, are providing unprecedented insights into large-scale bird migration, helping us map their journeys with remarkable precision. This technological advancement, however, is not a solitary endeavor. It thrives on the invaluable contributions of human experts and citizen scientists alike, whose observations and data serve as the essential foundation for AI’s learning and validation.
As AI continues to evolve, its potential to revolutionize bird conservation and deepen our connection with the natural world is immense. By embracing these tools and actively participating in citizen science, we can all play a part in safeguarding the future of our feathered friends.
Ready to make a difference? Start exploring the birds around you and consider contributing to citizen science initiatives. Every observation helps paint a clearer picture for AI, and for the future of our feathered friends.
Frequently Asked Questions
What is BirdCast and what is its primary purpose?
BirdCast is a collaborative project (Cornell Lab of Ornithology, Colorado State University, UMass Amherst) that utilizes machine learning and radar data from 143 sites across the U.S. Its primary purpose is to predict and track bird migration patterns, providing crucial information for understanding avian movements and conservation efforts.
How does AI help BirdCast distinguish birds from weather phenomena?
AI, specifically machine learning, is critical for this task. Algorithms are trained to identify distinct patterns in radar data. Weather phenomena like thunderstorms show irregular shapes and variable reflectivity, whereas migrating birds typically appear as more uniform and consistent patterns. This allows the system to filter out weather and focus on biological movements with high accuracy.
Why is citizen science still important in the age of AI ornithology?
Citizen science remains indispensable because human observations provide “ground truthing” and foundational data for AI models. These real-world sightings and acoustic data, collected by regular people, act as the “kernels” that allow AI models to learn, refine predictions, and ultimately create a more accurate picture of bird populations and movements. It also fosters public engagement with nature.
What are the practical conservation benefits of AI in ornithology?
AI-driven insights from projects like BirdCast directly contribute to conservation by providing accurate migration forecasts. This information allows cities and organizations to implement proactive strategies, such as dimming unnecessary lights during peak migration nights, significantly reducing bird collisions with buildings and saving countless lives.
