Generative AI-Enhanced Digital Twins for Predictive Ecosystem Management and Conservation

Vollständiger Abstract

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The escalating impacts of climate change and anthropogenic pressures on vulnerable ecosystems demand digital tools that make advanced modeling more accessible to conservation practitioners. This study presents a TRL-4 prototype that integrates a configurable Digital Twin (DT) core with a generative AI conversational interface for conservation-oriented modeling in Doñana National Park, Spain, a UNESCO World Heritage site facing significant environmental challenges. The main contribution is not the training of specific ecological forecasting models, but the validation of an end-to-end workflow that allows users to configure, execute, inspect, and interpret a predictive system through natural language. The prototype supports the prediction of conservation-relevant ecological indicators, including Iberian lynx population dynamics and waterbird abundance, using heterogeneous environmental, climatic, hydrological, and socio-demographic datasets. The architecture connects a structured YAML configuration, heterogeneous environmental and biological datasets, automated machine learning training, database-backed traceability, dashboard visualization, and SHAP-based interpretability. Through representative executions, the prototype demonstrates that non-technical users can select target and explanatory variables, configure preprocessing options, launch model training, generate predictions, and review their outputs without directly editing configuration files or running code. Although the predictive metrics obtained in selected runs remain preliminary and should be interpreted as diagnostics rather than evidence of general forecasting skill, the results show that conversational DTs can substantially reduce technical barriers to ecological modeling. By combining generative AI, cloud infrastructure, reproducible machine learning workflows, and explainable AI, the proposed architecture provides a strong foundation for future conservation decision-support systems that augment expert judgment while preserving human oversight, transparency, and critical interpretation.