Biodiversity conservation planning with reinforcement learning
9Throughout 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.
Speakers:
Daniele Silvestro
Computational biologist, ETH Zurich
Moderators:
David Thau
Global Data and Technology Lead Scientist, WWF
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