Conservation-focused digital twins: the current landscape and lessons learned from the Dangermond Preserve

As conservation challenges intensify, digital twins—virtual models that integrate real-time data and simulations—offer a powerful new approach to understanding and managing natural systems. In this paper, we explore how digital twin technologies, widely used in industry, can be adapted to meet the unique needs of conservation. We introduce a technical maturity framework tailored to conservation contexts, outlining stages from foundational data collection to Artificial Intelligence (AI) augmented management. Drawing on global examples and a detailed case study from The Nature Conservancy’s Jack and Laura Dangermond Preserve, we illustrate how conservation-focused digital twins (CFDTs) can support biodiversity monitoring, scenario planning, decision-making, and stakeholder engagement. While the field is still emerging, early efforts show promise in transforming how we collect, interpret, and act on environmental data. We highlight key lessons learned, including the importance of modular design, data governance, and community involvement. Ultimately, CFDTs have the potential to reduce the divide between the physical and the digital world, and thus data and decision-making, enabling more adaptive, inclusive, and effective conservation strategies.