Spatiotemporal patterns and environmental drivers of Physalia physalis strandings along mainland Portugal (northeastern Atlantic)

Physalia physalis is a cosmopolitan colonial organism frequently observed in Portuguese waters. The species has long tentacles, provided with cnidocytes, which release a strong poison when in contact with other organisms, and cause skin reactions and severe pain in humans. Events of rapid reproduction, common in these species, may have important economic and social consequences. This study explores for the first time, the relationships between long-term assessment of P. physalis strandings, using data from the GelAvista citizen science project, and environmental variables - Sea Surface Temperature (SST), wind direction and intensity, the North Atlantic Oscillation (NAO), and upwelling indices - along the Portuguese coast, using a Generalized Linear Mixed-Effects Model. Strandings of P. physalis were concentrated along the western coast and were rare in the south. More than 54% of the records corresponded to a single individual. Sightings peaked from November through May (winter and spring), mirroring the negative correlation with SST. Wind patterns strongly influenced stranding events: winds likely pushed colonies toward the shore, while calm conditions facilitated their arrival on beaches. In March 2018, an upwelling event in the south coast combined with a series of storms likely underlay the high stranding numbers observed in the southern area, with more than 50 individuals of P. physalis per record. A positive trend in sightings over the study period suggests that P. physalis occurrences may be rising, even after accounting for wind and SST, which might be linked to climate change. Our findings underscore the value of ongoing jellyfish monitoring via citizen science platforms like GelAvista and highlight the need for expanded high-resolution environmental datasets. Moreover, this work establishes a foundation for experimental studies to elucidate the mechanisms behind P. physalis strandings. For future research, time series techniques for imputing missing values may be utilized to enhance the completeness of environmental datasets and strengthen analytical robustness in subsequent studies focused on time series analysis. • We use citizen science data to understand stranding patterns of Physalia physalis • Data were combined with environmental variables using a Generalized Linear Mixed-Effects Model • Our findings suggest wind direction significantly control Physalia sighting patterns • Higher frequency of sightings is negatively correlated with Sea Surface Temperature • Positive trend in sightings over time is possibly linked to climate change effects