Pangeo-Fish and the Global Fish Tracking System: Scaling Biologging Analytics with Earth System Digital Twins for evidence-based policy support

The Global Fish Tracking System (GFTS) and Pangeo-Fish integrate biologging data with high-resolution environmental data in a digital-twin framework to address key challenges in marine conservation and fisheries management. Linking fish movement models with climate projections from Europe’s Destination Earth (DestinE) Climate Change Adaptation digital twin yields an evidence-based tool for decision support in habitat conservation and fisheries management under climate change. The implementation is built on the open-source Pangeo ecosystem and deployed on the DestinE platform.

Pangeo-Fish is an open-source package that ingests multiple biologging data types—including archival tags, pop-up satellite archival tags (PSATs), and acoustic telemetry detections. It processes time series observed by fish (e.g., depth, temperature, and light) together with geolocation constraints derived from external sources such as acoustic receiver networks and tag-based positioning. These heterogeneous observations are harmonised in a cloud-native workflow to support scalable track reconstruction and downstream habitat-relevant products.

Tracks are reconstructed using a Hidden Markov Model (HMM) geolocation approach that combines tag-recorded time series (e.g., depth, temperature, and light) with external geolocation constraints (e.g., acoustic detections), together with priors such as bathymetry and release/recapture information. Processing leverages cloud-native tools (Jupyter, Dask, Xarray) and chunked cloud-optimised storage (Zarr) for scalable analysis. A key design choice is the use of HEALPix as the base spatial grid and indexing scheme from ingestion to visualisation, enabling efficient path-likelihood evaluation on an equal-area, iso-latitude grid while avoiding distortive resampling. Environmental reference fields are primarily sourced from the Copernicus Marine Service, but the workflow can also ingest user-defined datasets. In addition, in situ observations (e.g., Argo float temperature) can be incorporated to represent uncertainty in the ocean physics fields used by the geolocation model and better account for model–data discrepancies.

The Pangeo-Fish workflow yields most-probable tracks and daily presence-probability maps, while GFTS supports aggregation of distributions for management-relevant analyses. GFTS intersects these outputs with climate projections from the DestinE Climate Change Adaptation Digital Twin to assess potential habitat exposure under climate change. GFTS has so far been demonstrated for Atlantic applications, while Pangeo-Fish has been extended to the Pacific Ocean, enabled by portable cloud-native processing and the availability of cloud-accessible datasets. As an early DestinE platform use case, this work illustrates how Earth system digital twins can be operationalised for reproducible, scalable biologging analytics to inform marine conservation and sustainable fisheries management.