International Journal of Marine Science, 2025, Vol.15, No.6, 287-291 http://www.aquapublisher.com/index.php/ijms 287 Research Article Open Access Predictive Displacement Theory (PDT): An AI-Assisted Framework for Forecasting Jellyfish Movement Based on Citizen Observations and Environmental Drivers C. Taklis Merman Conservation Expeditions Ltd., Edinburgh, United Kingdom Corresponding author: mermanconservation@gmail.com International Journal of Marine Science, 2025, Vol.15, No.6 doi: 10.5376/ijms.2025.15.0026 Received: 21 Aug., 2025 Accepted: 23 Oct., 2025 Published: 06 Nov., 2025 Copyright © 2025 Taklis, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Taklis C., 2025, Predictive Displacement Theory (PDT): an AI-assisted framework for forecasting jellyfish movement based on citizen observations and environmental drivers, International Journal of Marine Science, 15(6): 287-291 (doi: 10.5376/ijms.2025.15.0026) Abstract Jellyfish blooms are increasing in frequency and intensity across the Mediterranean Sea, posing growing challenges to tourism, fisheries, public safety, and coastal ecosystem monitoring. Despite the rise of citizen science platforms and the availability of real-time environmental data, no operational system currently exists to forecast jellyfish movement. This paper introduces the Predictive Displacement Theory (PDT), the first proposed framework for forecasting jellyfish drift by combining user-submitted sightings with environmental drivers such as wind, wave direction, sea surface currents, and atmospheric pressure. The concept is designed to operate through an AI-assisted application that ingests real-time observations and oceanographic data to generate short- and medium-term forecasts of jellyfish aggregations. As a proof of concept, the framework was retrospectively tested on the 2020~2023 Pelagia noctiluca blooms in Greece, with a focus on the Corinthian Gulf during 2021 and 2022, using Windy.com datasets and georeferenced observations from the iNaturalist platform and a Facebook group. Even without AI support, the model predicted southward jellyfish movement with up to 90% accuracy over five-day periods. These findings demonstrate the viability of PDT and its potential to evolve into the first real-time jellyfish forecasting system, supporting both ecological forecasting and timely public warning mechanisms. Keywords Jellyfish blooms; Predictive Displacement Theory; AI-assisted forecasting; Citizen science; Coastal ecosystem management 1 Introduction Jellyfish blooms have emerged as one of the most visible and disruptive phenomena in coastal marine ecosystems, particularly in semi-enclosed basins such as the Mediterranean Sea. In recent decades, reports of large jellyfish aggregations have increased in both frequency and spatial extent, attributed to a combination of climate change, overfishing of natural predators, eutrophication (Fernández-Alías et al., 2024), and changing oceanographic conditions (Gravili, 2020). These blooms not only disrupt local food webs but also interfere with human activities, including fisheries (Palmieri et al., 2015), aquaculture operations, power plants, and especially tourism, where jellyfish stings can deter swimmers and reduce coastal revenue. In the Mediterranean, species such as Pelagia noctiluca have become increasingly dominant (Bordehore, 2023) during summer months, forming large swarms that drift with currents and winds. However, despite their growing impact (Praved et al., 2021), there is currently no operational system capable of forecasting jellyfish movement in real time (Marambio et al., 2021). While citizen science platforms such as iNaturalist have improved spatial data availability, and while environmental datasets on wind, waves, and currents are widely accessible through services like Windy and Copernicus Marine, these data streams remain unconnected in any unified forecasting framework (Avazbek Furqat o’g’li et al., 2022) for jellyfish behavior. This paper introduces the Predictive Displacement Theory (PDT) as a novel theoretical and technical framework aimed at filling this gap. PDT proposes that jellyfish movement in coastal systems follows semi-deterministic paths (Edelist et al., 2022) influenced by physical oceanographic forces and initial population inputs. By leveraging citizen-submitted sightings as anchor points and combining them with dynamic environmental vectors, PDT offers
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