Reconciling top-down and bottom-up conservation priorities with reinforcement learning from human feedback

Daniele Silvestro, Stefano Goria, E-Ping Rau, Thomas Sterner, Alexandre Antonelli

To meet the Kunming-Montreal Global Biodiversity Framework, spatial planning must upscale area-based conservation, while ensuring equitable governance and local community integration. Historically, a deep divide has persisted between data-driven, top-down systematic conservation planning, often performed at large spatial scales, and participatory, bottom-up local initiatives. Here, we propose a novel framework that bridges this gap by harnessing the power of human and artificial intelligence. By deploying a technique called Reinforcement Learning with Human Feedback in our software CAPTAIN (Conservation Area Prioritization through Artificial INtelligence), our approach can process vast biophysical and socioeconomic data to create fine-tuned conservation strategies that are both data-driven and sensitive to local realities. The methodology outlined here provides a promising avenue for scalable, effective, and equitable biodiversity conservation.