Q&A with Andrew Farnsworth, a Cornell Lab of Ornithology scientist, about BirdCast, a project that uses AI to process weather data and predict how birds migrate
Malena Carollo / The Markup :
Context & Ripple Effects
BirdCast sits within the Cornell Lab of Ornithology’s broader use of digital observation tools: eBird’s large-scale sighting database made bird observations more accessible, while the Lab’s Merlin app expanded species identification from photos and sound.
The project applies AI to weather inputs for migration forecasts rather than merely cataloging sightings. Related coverage of open-source flight-call detection software shows a complementary effort to turn ecological signals into machine-readable data.
First-order effects
- BirdCast gives researchers and bird-focused users a forecast-oriented view of migration, shifting AI’s role from identifying observations to anticipating when migratory movement may occur.
- The Cornell Lab’s work becomes more dependent on combining environmental data with domain expertise, alongside its existing identification and observation tools.
Second-order effects
- Forecasts can help direct when and where observers collect records, potentially improving the timing and usefulness of citizen-science contributions to bird-monitoring datasets.
- Projects that detect calls in recordings and projects that predict migration from weather data can reinforce one another: forecasts can target audio monitoring, while detections can help assess forecast performance.
Third-order effects
- If these tools prove reliable across conditions, wildlife research may increasingly be organized around linked data pipelines—environmental forecasting, automated sensing, and public observation—rather than isolated manual surveys.
- The durable constraint will be validation and interpretation: AI can scale ecological monitoring, but conservation decisions still require evidence that model outputs generalize beyond the data and locations used to build them.
The trend: BirdCast is one example of AI moving ecological research from automated identification toward predictive, data-integrated monitoring.