Energy‐Aware AI for Landscape‐Scale Conservation: A Digital Twin Architecture for the Greater Yellowstone Ecosystem

Artificial intelligence offers tremendous potential for landscape-scale biodiversity conservation, yet the significant energy consumption of large-scale AI models creates a fundamental paradox: the computing resources required to train and deploy these systems add to the very environmental degradation they seek to prevent. This paper proposes a multi-level, energy-aware AI architecture for constructing ecosystem digital twins that enables prescriptive, rather than merely descriptive or predictive, conservation management. The proposed framework classifies conservation tasks across three levels: classic machine learning for continuous environmental monitoring and species distribution prediction; deep learning for perception-oriented tasks such as computer vision and bioacoustics analysis; and foundation models for cross-domain synthesis and stakeholder interaction, where their capabilities are irreplaceable. We apply this architecture to a conceptual digital twin of the Greater Yellowstone Ecosystem, demonstrating how multi-tiered AI integration can model ecological systems spanning wolves, elk, vegetation, beavers, and hydrology to generate actionable, prescriptive insights concerning conservation. A comparative energy footprint analysis estimates that the tiered approach decreases computational energy consumption by approximately 62–74% relative to a foundation-model-centric baseline, while sustaining or improving conservation decision quality. This work addresses a key gap in the literature by providing the first integrated architectural framework that explicitly optimizes the trade-off between AI capability and environmental cost for landscape-scale conservation applications, supplying a replicable blueprint for resource-constrained conservation organizations worldwide.