Towards Self-Adaptive Data Management in Digital Twins for Biodiversity Monitoring.

Biodiversity monitoring is concerned with keeping track of different species in an ecosystem over time, with respect to their abundance, distribution and diversity. Environmental digital twins used for biodiversity monitoring share characteristics with industrial digital twins, but face additional challenges in connecting data and models: Biodiversity data is often not livestreamed, interventions are slow and require human interaction, and the scientific knowledge about species and their habitats constantly evolves. Today, environmental digital twins offer little automation support, or any support to help scientists link species observations to assumptions about biodiversity. This paper presents an application of structural self-adaptation, originally developed in the industrial domain, to environmental digital twins. We show how structural self-adaptation enables to autonomously adapt monitored assumptions to changes in the available data sources, and further discuss how digital twins can adapt to changes in the domain knowledge. A first evaluation is given based on underwater cameras in the Oslo Fjord.