| Summary: | Frequent jellyfish blooms cause human health issues and closures of coastal areas, impacting different economic sectors like tourism, fisheries, aquaculture farms and industry. Understanding the drivers of jellyfish bloom and predicting their occurrence is therefore essential to develop effective management plans. The Portuguese Man-of-War (Physalia physalis) is a dangerous cosmopolitan siphonophore and its ecology remains largely understudied. The objective of this study is to understand the main environmental drivers (e.g., temperature, productivity, wind and ocean patterns) that explain the occurrence of P. physalis at a macroecological scale (the North Atlantic Ocean) and at a regional scale (Faial Island from the Azores archipelago), and to predict its distribution and temporal trends. We implemented machine learning modelling that fed on high-resolution environmental data and occurrence data describing its distribution in the North Atlantic Ocean and long-term temporal variability in the Faial Island (Azores). Models retrieved high accuracy scores and showed that the distribution of P. physalis is mainly explained by primary productivity, temperature and currents direction at the macroecological scale and by primary productivity and wind patterns at the regional scale. The models also showed a higher probability of occurrence on both Atlantic coasts and offshore North-northwest Atlantic. Models fed on temporal datasets demonstrate decadal fluctuations rather than significant increases over time, contradicting the previously established hypothesis that jellyfish blooms are increasing. By using species distribution modelling, we provide a better understanding on how environmental variability shapes the occurrence of P. physalis at different spatial and temporal scales (macroecological and regional), which can be considered in management plans and policies. In the future, projected global warming and decreased primary productivity in the North Atlantic may cause significant poleward shifting of this species, ...
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