The Doñana Digital Twin: integrating heterogeneous data into iterative, mechanistically grounded forecasts of biodiversity change
Year: 2026
Type: preprint
Abstract: Natural ecosystems are increasingly impacted by multiple global-change drivers, and these impacts propagate non-linearly from individuals to populations and to communities. To anticipate ecological change under such complexities and mitigate negative impacts, we require integrated, mechanistically-grounded approaches to prediction. For mitigation strategies to be adaptive, predictions must also be updated efficiently as new evidence becomes available. In many protected areas, decades of heterogeneous monitoring data are available, but remain siloed within research groups or projects and are rarely turned into integrated forecasts. Here we present an ecological digital twin of Doñana Natural Area, a Mediterranean biodiversity hotspot under increasing climate and land-use pressures. The digital twin combines long-term biodiversity data with weather, hydrological and remote-sensing products. This data is then used to link demographic, abundance and occurrence models to interspecific interactions, which results in community and ecosystem dynamics. Annual forecasts are generated under global-change scenarios, and uncertainty is explicitly carried through every stage of this chain, from driver to outcome. The digital twin is hosted open-access on the LifeWatch ERIC e-infrastructure, thus ensuring transferability and continuity. Annual evaluation of forecast skill against benchmark models, open forecast submissions by different user groups, and a web interface support continuous updating and decision-making. Importantly, the digital twin is also built on standardized workflows, allowing new models, species, and data streams to be incorporated rapidly and consistently. This design makes the approach directly scalable: the same infrastructure can be extended to other protected areas that already hold numerous ecological datasets but currently lack the integrated products needed to translate that data into actionable ecological knowledge.
DOI: 10.32942/x27q4j
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: green
OpenAlex ID: W7223054620