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Abstract The surge of pelagic Sargassum in the Intra-America Seas, particularly the Caribbean Sea, since the early 2010s has raised significant ecological concerns. This study emphasizes the need for a mechanistic understanding of Sargassum dynamics to elucidate the ecological impacts and uncertainties associated with blooms. By introducing a novel transport model, physical components such as ocean currents and winds are integrated with biological aspects affecting the Sargassum life cycle, including reproduction, grounded in an enhanced Maxey–Riley theory for floating particles. Nonlinear elastic forces among the particles are included to simulate interactions within and among Sargassum rafts. This promotes aggregation, consistent with observations, within oceanic eddies, which facilitate their transport. This cannot be achieved by the so-called leeway approach to transport, which forms the basis of current Sargassum modeling. Using satellite-derived data, the model is validated, outperforming the leeway model. Publicly accessible codes are provided to support further research and ecosystem management efforts. This comprehensive approach is expected to improve predictive capabilities and management strategies regarding Sargassum dynamics in affected regions, thus contributing to a deeper understanding of marine ecosystem dynamics and resilience. 

Ulam’s method is a popular discretization scheme for stochastic operators that involves the construction of a transition probability matrix controlling a Markov chain on a set of cells covering some domain. We consider an application to satellite-tracked undrogued surface-ocean drifting buoy trajectories obtained from the National Oceanic and Atmospheric Administration Global Drifter Program dataset. Motivated by the motion of Sargassum in the tropical Atlantic, we apply Transition Path Theory (TPT) to drifters originating off the west coast of Africa to the Gulf of Mexico. We find that the most common case of a regular covering by equal longitude–latitude side cells can lead to a large instability in the computed transition times as a function of the number of cells used. We propose a different covering based on a clustering of the trajectory data that is stable against the number of cells in the covering. We also propose a generalization of the standard transition time statistic of TPT that can be used to construct a partition of the domain of interest into weakly dynamically connected regions.