International Journal of Marine Science, 2025, Vol.15, No.6, 287-291 http://www.aquapublisher.com/index.php/ijms 290 the southern Corinthian Gulf coastlines consistently received aggregations 2 to 4 days after initial observations were reported along southern shores. While the forecasting in this phase did not use AI or machine learning, the results demonstrated strong correspondence between predicted and actual bloom displacement. 4 Discussion The retrospective application of the Predictive Displacement Theory (PDT) to the Pelagia noctiluca blooms in the Greece from 2020 to 2023 and specifically in the Corinthian Gulf from 2021 to 2022, provides a promising indication that jellyfish movement can be forecast with reasonable accuracy using a combination of citizen science data and environmental information. Despite the absence of artificial intelligence in this initial phase, the model’s ability to predict southward drift with up to 90% accuracy over a five-day period demonstrates the potential of PDT as a foundation for real-time forecasting tools. The integration of citizen observations with open-access environmental data streams such as those provided by Windy.com offers a cost-effective and scalable method for ecological monitoring. Public contributions through platforms like iNaturalist not only enhance spatial coverage but also improve temporal resolution, which is critical for capturing dynamic bloom behavior. The Corinthian Gulf’s physical characteristics, including its semi-enclosed geography and prevailing current and wind patterns, make it particularly suitable for applying the PDT framework. The consistent pattern of northerly winds driving jellyfish swarms toward the southern coastline is well captured by the model, reflecting the semideterministic displacement corridors hypothesized in the theory. Looking ahead, incorporating artificial intelligence and machine learning techniques will be essential for refining the model, enabling automated data processing, and producing probabilistic forecasts. Such advancements could facilitate real-time alerts for stakeholders, including coastal managers, fisheries, and the general public. To achieve this, future development should focus on expanding training datasets, improving data validation protocols, and integrating additional environmental variables such as vertical water column profiles and biological behavior patterns. Limitations of the current study include the assumption that jellyfish move primarily as passive drifters, without accounting for vertical migration, active swimming, or biological life cycle events. Furthermore, finer-scale coastal geomorphological features were not included in the model, which may affect local swarm behavior nearshore. Overall, this study lays the groundwork for the first operational jellyfish forecasting system. By combining community science with environmental modeling, PDT offers a novel approach to managing the increasing ecological and socio-economic impacts of jellyfish blooms in the Mediterranean and beyond. While the retrospective validation of PDT demonstrates strong predictive potential, several limitations should be acknowledged. First, the reliance on citizen science introduces seasonal and geographic biases, as reports are concentrated during summer months and near populated coastal areas, leaving offshore and winter dynamics underrepresented. Second, data sparsity and uneven spatial coverage may limit the accuracy of predictions in undersampled regions. Third, the integration of real-time AI poses computational and logistical challenges, including automated data validation, continuous assimilation of environmental streams, and the infrastructure required to support large-scale forecasting applications. Addressing these limitations will be critical for scaling PDT into a fully operational forecasting tool across the Mediterranean. Acknowledgements I gratefully acknowledge the valuable contributions of citizen scientists who submitted jellyfish observations to the iNaturalist platform, and the Facebook group “Jellyfish in Greece”, without whom this study would not have been possible. References Avazbek Furqat o’g’li, A., Kalandarovna O.X., and Rubio Fátima S., 2022, Jellyfish diversity, trends and patterns in Southwestern Mediterranean Sea: a citizen science and field monitoring alliance, Journal of Plankton Research, 44(6): 819-837. https://doi.org/10.1093/plankt/fbac057
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