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Forecasting and understanding bird migration with process-guided deep learning

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    • Date
    8 October 2025
    Timeframe
    16:00 - 17:00 CEST Geneva
    Duration
    60 minutes

    The seasonal migrations of billions of birds are among the most spectacular natural phenomena on Earth, but they are increasingly threatened by the impacts of human activities, such as light pollution, climate change, and collisions with wind turbines and aircraft. To effectively protect migration systems, we need models that can accurately forecast large-scale movements and help us better understand how birds respond to environmental conditions. This talk explores the benefits and challenges of process-guided deep learning, combining ecological principles with neural networks to model bird migration across continents based on weather radar data. In the first part, I will present a migration forecast model that augments a Eulerian movement model with flexible neural network components to predict bird fluxes across time and space. In the second part, I will showcase how this hybrid forecast model, when interpreted carefully, can provide novel insights into the decision-making of migrating birds across diverse environments. The talk concludes with a broader perspective on the potential of process-guided deep learning in ecology and Earth science, highlighting applications where domain knowledge is partial yet essential for building models that are accurate, robust, and interpretable.

     

    Learning Objectives:

    By the end of the session, participants will be able to:

    • Describe the major threats to bird migration and the importance of large-scale migration modeling.
    • Explain the principles of process-guided deep learning and how ecological knowledge can be integrated into neural network models.
    • Interpret outputs from a hybrid Eulerian-neural network model to assess bird flux patterns across continents.
    • Critically evaluate the benefits and limitations of combining ecological theory with AI for robust and interpretable ecological forecasting.
    • Propose potential applications of process-guided deep learning in other ecological or Earth system contexts where domain knowledge is partial.

    Recommended Mastery Level / Prerequisites:
    Mastery Level: Intermediate to Advanced (suitable for graduate students, PhD candidates, and researchers in ecology, environmental science, or data science).

    Prerequisites:

    • Basic understanding of ecological processes and bird migration.
    • Familiarity with machine learning or deep learning concepts.
    • Comfort with interpreting model outputs and spatial-temporal data.
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