Coupling hydrodynamic drifting simulations and seasonal demographics to unmask the drivers of jellyfish blooms

Tools

Cant, James and Ellingsen, Ingrid and Laverick, Jack H. and Majaneva, Sanna and Dierking, Jan and Aberle, Nicole and Javidpour, Jamileh and Jones, Owen R. (2025) Coupling hydrodynamic drifting simulations and seasonal demographics to unmask the drivers of jellyfish blooms. Journal of Applied Ecology, 62 (11). pp. 2971-2986. ISSN 0021-8901 (https://doi.org/10.1111/1365-2664.70186)

[thumbnail of Cant-etal-JAE-2025-Coupling-hydrodynamic-drifting-simulations-and-seasonal-demographics-to-unmask]
Preview
 Text. Filename: Cant-etal-JAE-2025-Coupling-hydrodynamic-drifting-simulations-and-seasonal-demographics-to-unmask.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (1MB)| Preview

Abstract

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. Predicting jellyfish blooms is crucial for managing and mitigating their ecological and economic impacts, but the complex life cycles and cryptic life stages exhibited by most jellyfish species largely preclude accurate predictions of their temporal and spatial occurrence. Here, we introduce a framework, combining state-of-the-art hydrodynamic simulations and periodic population modelling approaches, to simulate 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 this framework can provide valuable insights for resolving key gaps in our understanding of the drivers of bloom events. Indeed, by doing so, this tool will help to guide the future collection of data needed to predict the locations and timings of their formation. Synthesis and applications. Crucially, the framework we present here offers an approach for identifying the, to date, unknown locations of polyp beds; a key parameter in enhancing our capacity to accurately predict the occurrence of jellyfish blooms. Accordingly, this framework represents a key decision-support tool for mitigating the socio-economic impacts of bloom formation.

ORCID iDs

Cant, James, Ellingsen, Ingrid, Laverick, Jack H. ORCID logoORCID: https://orcid.org/0000-0001-8829-2084, Majaneva, Sanna, Dierking, Jan, Aberle, Nicole, Javidpour, Jamileh and Jones, Owen R.;
CORE (COnnecting REpositories)