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Climate variability is increasing the frequency and impacts of marine hazards, yet early warning systems often lack mechanisms to translate environmental signals into timely governance responses. Here, we examine how local ecological knowledge can be operationalized within a national hazard monitoring framework using jellyfish blooms as a model marine hazard. We analyzed 15 years (2010–2024) of weekly, fisher-reported observations from Korean coastal waters, focusing on Aurelia coerulea and Nemopilema nomurai. From these weekly reports, we calculated occurrence rate, defined as the percentage of respondents reporting jellyfish presence, and aggregation frequency, defined as the percentage reporting jellyfish aggregations. These indices were interpreted as operational risk indicators rather than standardized abundance estimates. Their utility was evaluated by testing temporal alignment with official national jellyfish alerts and by quantifying species-specific lead–lag structures. Distinct bloom dynamics emerged: A. coerulea formed persistent aggregations peaking from late spring to summer, whereas N. nomurai exhibited low background occurrence punctuated by abrupt mass events in late summer to autumn. Monthly sea surface temperature (SST) showed positive associations with fisher-reported occurrence and aggregation frequencies for both species (p < 0.05). Generalized Additive Models indicated a long-term weakening of A. coerulea bloom intensity and an earlier seasonal shift in N. nomurai peaks, consistent with warming-related phenological change. Alert timing closely tracked A. coerulea peaks, while N. nomurai alerts preceded peaks by 4–6 weeks, reflecting differences in life history and cross-boundary transport. By linking weekly fisher-reported bloom signals with official national jellyfish alerts, this study shows how local ecological knowledge can provide decision-relevant information for bloom timing and operational response to jellyfish hazards under changing climate conditions.
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Choi et al. (2026) studied this question.
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