International Journal of Marine Science, 2025, Vol.15, No.6, 287-291 http://www.aquapublisher.com/index.php/ijms 288 the basis for an AI-powered forecasting system (Castro‐Gutiérrez et al., 2024) capable of predicting jellyfish aggregations (Marambio et al., 2021) over short (1~3 days) and medium (up to 10 days) timescales. To test the viability of this framework, we retrospectively applied PDT principles to a well-documented case: the widespread blooms of Pelagia noctiluca in the Greece but focused on the Corinthian Gulf between 2021 (Taklis, 2022) and 2022 (Taklis, 2023). Using geo-referenced observations from a facebook group, iNaturalist platform and environmental data from Windy.com, we simulated the southward movement of jellyfish swarms under realistic meteorological conditions (Berline et al., 2013). While the test did not include machine learning, it successfully replicated bloom drift with high accuracy, supporting the foundation of PDT as a predictive tool (Gauci et al., 2020). This paper presents the theoretical foundations of PDT, outlines its operational structure, and discusses the potential for its evolution into the first real-time jellyfish forecasting system, designed for use by scientists, coastal managers, tourism operators, and the general public. Unlike traditional hydrodynamic or ecological models, which depend heavily on numerical simulations and extensive in-situ oceanographic datasets, PDT provides a lightweight and adaptive framework by directly coupling citizen science observations with real-time environmental drivers. This integration of participatory data and openaccess oceanographic streams represents a novel approach to forecasting jellyfish movement, bridging the gap between community monitoring and applied ecological modeling. 2 Methodology 2.1 Overview of the PDT framework The Predictive Displacement Theory (PDT) (Castro‐Gutiérrez et al., 2024) is based on the hypothesis that jellyfish blooms do not disperse randomly (Edelist et al., 2022) but follow movement corridors shaped by environmental forces, such as wind, sea surface currents, and wave dynamics (Castro-Gutiérrez et al., 2022). PDT treats each jellyfish sighting as an origin point for displacement modeling, where jellyfish swarms are passively transported through marine physical vectors (Fossette et al., 2015). The proposed system is designed to operate as a modular forecasting tool that combines three data layers: Citizen science input: georeferenced observations submitted by users (Gutiérrez-Estrada et al., 2021) via a mobile or web-based app. Environmental data streams: real-time meteorological and oceanographic parameters from open-access platforms. AI or rule-based simulation engine: a model that processes the above inputs to generate spatial forecasts of jellyfish movement. The initial implementation of PDT relies on vector-based simulations, but future versions are intended to incorporate AI models trained on historical bloom data. 2.2 Data Sources 2.2.1 Citizen observations Jellyfish sighting data were sourced from the Facebook group “Jellyfish in Greece,” a public citizen science community where users submit reports including date, location, photographs, and species-level identifications. For the purposes of this study, records of Pelagia noctiluca from the Corinthian Gulf between 2021 and 2022 were extracted, filtered for accuracy, and aggregated to identify spatiotemporal patterns and likely bloom initiation zones. Data quality was ensured through a multi-step filtering process. Duplicate reports and those lacking geolocation or time stamps were removed. Only records supported by photographic evidence were retained, and species-level identifications were cross-validated using community consensus on iNaturalist and additional expert review. This reduced the dataset to 150 verified observations between 2020 and 2023, with higher concentrations during June to September. Reports were aggregated into weekly intervals and mapped to subregions of the Corinthian Gulf to establish bloom initiation zones and subsequent displacement patterns. 2.2.2 Environmental data Environmental inputs were retrieved from Windy.com and related APIs, including: Wind speed and direction at 10 m altitude; Surface current velocity and direction; Wave height, period, and direction; Sea surface temperature (SST); and Atmospheric pressure maps.
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