TwinEco: A unified framework for dynamic data-driven digital twins in ecology

A Digital Twin (DT) is a virtual replica of a physical object or process that is continuously updated at a certain frequency and can steer change on the physical system, enabling a seamless integration of observation, understanding, and action. Although initially applied primarily in industry, DT is emerging as a powerful tool in ecology, offering new possibilities for dynamic simulations of change in the biosphere. However, since DTs are relatively new in this field, there is currently no standard framework to guide their conceptualisation and development. Thus, DTs in ecological applications are already experiencing fragmentation in software concepts and design philosophies, leading to incompatibilities across DT implementations. This fragmentation risks undermining the progress and potential of the DT concept in ecology. A unifying framework, such as TwinEco, can address these discrepancies and establish a cohesive foundation for the effective adoption and integration of DTs across ecological domains. TwinEco is a modular framework designed to aid and harmonise ecologists' efforts to build DTs. In doing so, TwinEco focuses on three major design goals: 1. Modularity, flexibility, and interoperability of DTs facilitated through distinct “components” nested within DT “layers”. 2. Dynamic modelling of ecological processes and states that evolve over time. 3. Linking ecological modelling to downstream actions or decisions made on the ecological object or process of study. TwinEco's architecture builds upon the feedback loops and state management strategies introduced in the Dynamic Data-Driven Application Systems (DDDAS) paradigm, which has already inspired many DTs across scientific domains. We also discuss the usefulness and ease-of-use of TwinEco by demonstrating its applicability to computational case studies and suggesting future recommendations to the community of data infrastructure builders and modellers in regards to open considerations. By introducing a shared terminology and emphasising model-data fusion, TwinEco highlights the importance of a unified framework to avoid fragmentation in the burgeoning field of ecological digital twinning. • The novelty of TwinEco is introducing a unified framework to build ecological digital twins (DTs). • TwinEco defines DT layers and components and explains their interactions. • The framework features high modularity and flexible design of DT software. • TwinEco directly supports biodiversity conservation and ecosystem management. • It combines DT scenes with ecological and hydrological models, offering actionable insights for community-level adaptation strategies.