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

Sep 2026 · Environmental Research Letters
Species Distribution and Climate Change

Abstract

Abstract 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.

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