Coupling hydrodynamic drifting simulations and seasonal demographics to forecast the occurrence of jellyfish blooms

Although jellyfish are an important component of coastal marine communities, their public perception is often tainted by their proclivity for aggregating in vast numbers, known as jellyfish blooms. Jellyfish blooms occur worldwide and are associated with major economic ramifications, particularly throughout the fisheries, aquaculture, and tourism sectors. Forecasting jellyfish blooms is critical in order to manage and mitigate their ecological and economic impacts, but the complex life cycles and cryptic life stages exhibited by most jellyfish species largely precludes accurate predictions of their temporal and spatial occurrence. Here we introduce a predictive framework, combining state-of-the-art hydrodynamic simulations and periodic population modelling approaches, to forecast spatial and temporal patterns in the formation of jellyfish blooms. While this framework is sufficiently flexible for accommodating various bloom-forming jellyfish species and impacted coastal regions worldwide, we focus on moon jellyfish ( Aurelia aurita ) populations within the Baltic Sea as an illustrative example. We emphasise how, through the iterative process of forecasting and model validation, this framework can provide valuable insights for resolving key gaps in our understanding of the drivers of bloom events. Indeed, our framework offers an approach for identifying the, to date, unknown locations of polyp-beds; a crucial parameter in enhancing our capacity to accurately predict the occurrence of jellyfish blooms. Accordingly, our framework, represents a key decision-support tool for mitigating the socioeconomic impacts of bloom formation.