TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology

Abstract

The burgeoning interest in digital twin (DT) technology presents a transformative potential for ecological modelling, offering new ways to model the complex dynamics of ecosystems. This paper introduces the TwinEco framework, designed to mitigate fragmentation in the development and deployment of DT applications in ecology. Compared to traditional modelling frameworks, TwinEco emphasises modularity and flexibility by introducing "layers" and "components" for DTs, accommodating diverse ecological applications without necessitating the deployment of all components. This modular approach ensures adaptability and scalability, promoting interoperability and integration with broader initiatives like Destination Earth. Digital twins in ecology offer significant advancements over traditional approaches by explicitly modelling changing processes and states over time, integrating extensive data, and enabling real-time feedback loops by actuating events or policies in the "real-world". The framework's capacity to adjust to changing environmental conditions enhances its predictive accuracy and responsiveness. This paper highlights the necessity for a unified framework to prevent divergent interpretations and ensure the interoperability of DT applications across ecological domains. Future recommendations include expanding case studies to demonstrate the framework's applicability, assessing the potential of Dynamic Data-Driven Application Systems (DDDAS) paradigm within ecological DTs, and exploring interactions between components to optimise performance. Emphasising model-data fusion and fostering a shared terminology within the ecological community are crucial for the framework's success. TwinEco aims to provide a robust foundation for ecological digital twins, enabling timely, data-driven decision-making to address global environmental challenges.