Biologging and telemetry are valuable tools for understanding animal migration and behavior in the wild. With technological advancements, the amount of data collected from tagged animals has surged, today often encompassing tens of millions of positions and hundreds of millions of sensor readings, such as high resolution acceleration data. Making sense of these large spatial and behavioural data-streams further require underlying layers of environmental and habitat-data. Statistical analysis of such spatio-temporal datasets often demand handling of complex correlation structures, resulting in computationally intensive analysis that may take days to run on ordinary desktop computers. This significantly hampers research progress and workflow efficiency, often resulting in valuable data being underexploited due to researchers' limited computational resources and time constraints. This project aims to leverage high-resolution tracking and biologging data across multiple fish species from various environments to explore the drivers of behavior in relation to habitat and environmental factors.