FAIR digital twins for biodiversity: enabling data, model, and workflow integration.

The biodiversity crisis demands computational tools to integrate and analyse complex, disparate data and models. This paper presents the concept of FAIR Digital Twins (FDTs) and, drawing on the work of the Biodiversity Digital Twin (BioDT) project (2022-2025), demonstrates how combining Digital Twins with FAIR principles (Findable, Accessible, Interoperable, and Reusable) can transform biodiversity research and decision-making. We show strategies for integrating heterogeneous data, models, and computational workflows within a FAIR framework, paving the way for operational FDTs. The BioDT project developed ten prototype digital twins addressing a critical range of challenges, including grassland and forest dynamics, bird monitoring, ecosystem services, and crop wild relative genetic resources. We discuss implementation challenges such as data fragmentation, semantic interoperability, and operational complexity. Critically, we highlight the opportunities for dynamic adaptation, modular workflows, and cross-domain collaboration, detailing how tools like Research Object Crate (RO-Crate) operationalise FAIR principles for metadata packaging and standardisation. This convergence of Digital Twins with FAIR principles offers a scalable and reusable approach to advancing biodiversity modeling and simulation, providing a robust foundation for evidence-based policy decisions.