Enhancing Pangeo-Fish with HEALPix Convolution: Impact Evaluation and Benefits

The Pangeo-Fish project processes biologging data to analyze fish movement and migration patterns.  While SciPy’s convolution methods are robust, they are not optimized for handling spherical datasets inherent to Earth system science. To address this limitation, we propose the integration of HEALPix convolution, a method designed for spherical operations, into Pangeo-Fish.HEALPix convolution offers distinct advantages for geophysical data analysis, particularly when dealing with spherical datasets in Earth system science. It uses the HEALPix pixelization as a core Discrete Global Grid System (DGGS), which ensures equally-sized pixels globally, removing distortions common in flat projections. This consistency is crucial for maintaining the physical relevance of convolutions across locations. Additionally, HEALPix’s dyadic property supports flexible, multiscale resolution adjustments, allowing for downscaling while preserving accuracy. Such scalability is essential for studying oceanic environments where areas of interest, like coastal zones and basins, are often resolution-dependent.Our approach evaluates the performance of HEALPix convolution in comparison to traditional SciPy methods, focusing on its ability to enhance the accuracy of habitat mapping and migration pathway modeling for fish. This integration is particularly relevant within the Global Fish Tracking System (GFTS), which operates under the European Union’s Destination Earth (DestinE) initiative. GFTS utilizes datasets from Copernicus Marine Services and the European Tracking Network (ETN) to model fish habitats, spawning grounds, and migration swimways. HEALPix convolution strengthens the pangeo-fish’s capacity for studying Species such as tuna and eel that exhibit large-scale, transoceanic migrations.    In conclusion, this work highlights the transformative potential of HEALPix convolution in spherical data processing. By integrating this innovative method, Pangeo-Fish can provide more accurate, scalable, and actionable insights into fish behaviors and habitats, contributing to sustainable management practices and conservation strategies globally.