Robust Algorithms for Capturing Population Dynamics and Transport in Oceanic Variables along Drifter Trajectories using Linear Dynamical Systems with Latent Variables

Abstract

The blooms of Noctiluca in the Gulf of Oman and the Arabian Sea have been intensifying in recent years posing a threat to regional fisheries and the long-term health of an ecosystem supporting a coastal population of nearly 120 million people. We present the results of a microscopic data analysis to investigate the onset and patterns of the Noctiluca (mixotrophic dinoflagellate Noctiluca scintillans) blooms, which form annually during the winter monsoon in the Gulf of Oman and in the Arabian Sea. Our approach combines methods in physical and biological oceanography with machine learning techniques. In particular, we present a robust algorithm, the variable-length Linear Dynamic Systems (vLDS) model, that extracts the causal factors and latent dynamics at the microscopic population-level along each individual drifter trajectory, and demonstrate its effectiveness by using it to test and confirm previously benchmarked macroscopic scientific hypotheses. The test results provide microscopic statistical evidence to support and recheck the macroscopic physical and biological Oceanography hypotheses on the Noctiluca blooms; it also helps identify complementary microscopic dynamics that might not be visible or discoverable at the macroscopic scale. The vLDS model also exhibits a generalization capability (inherited from a machine learning methodology) to investigate important causal factors and hidden dynamics associated with ocean biogeochemical processes and phenomena at the population-level.