A versatile Lagrangian-data aggregation framework for marine biological dispersal studies

Lagrangian particle dispersal simulations are widely used for studying directed connectivity between different locations in the ocean. They are used, both, for the understanding of ocean physics and for interdisciplinary questions. One biological example is the dispersal of passively drifting marine organisms.

The typical modus operandi of such “bio-physical” studies is to design an underlying Lagrangian simulation in close synchronisation with a specific biological research question. This leads to a conflation of concerns between physical and biological aspects of the study. This conflation might result in repeated and slow development cycles of re-calculation for different scenarios and hence inhibit scientific progress.

We aim at improving the separation of concerns between biological and physical components for bio-physical Lagrangian studies, by aggregating physical Lagrangian data into directed multigraphs encoding locations as nodes and multiple parallel pathways as directed edges. Those graphs condense the physics-based information on directed oceanic relations and thus serve as a basis for simultaneously answering various biological questions on connectivity. As the proposed aggregation retains the distinction of different pathways between locations, it can, to some extent, also provide information of underway environmental conditions. This greatly enhances the range of applications of our approach over existing aggregations of Lagrangian data as connectivity probability graphs.

We present a specific set of biological case studies — the multi-year spreading of two oyster diseases in the North Sea — and develop a framework that facilitates efficiently and simultaneously testing multiple biological hypotheses for marine diseases of various species based on the same processed physical data set.