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Biodiversity conservation planning with reinforcement learning

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  • Date
    15 September 2025
    Timeframe
    16:00 - 17:00 CEST
    Duration
    60 minutes

      Throughout the long evolutionary history of life, species of all kingdoms have undergone staggering diversification and faced countless environmental changes and extinction events. Today, with over a million species threatened with extinction, biodiversity is facing unprecedented challenges, urging the need for conservation policies that maximize its protection and sustain its manifold contributions to people. We introduce a reinforcement learning framework to optimize biodiversity conservation and restoration policies. We show that multi-objective optimization can help guiding ecological restoration to meet both biodiversity and climate targets, while integrating socio-economic data and constraints. Coupling artificial intelligence with biological mechanistic models holds great promise for advancing our understanding of biodiversity and for improving the protection of nature in a rapidly changing world.

      Learning Objectives:

      • Explain the current challenges facing biodiversity conservation that necessitate optimized policy approaches.
      • Describe how a reinforcement learning framework can be applied to optimize biodiversity conservation and restoration policies.
      • Analyze the benefits of using multi-objective optimization within conservation planning, specifically regarding the integration of biodiversity, climate, and socio-economic factors.
      • Evaluate the potential of coupling artificial intelligence with biological mechanistic models to enhance biodiversity understanding and protection.

      Recommended Mastery Level / Prerequisites:
      None

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