Jellyfish blooms represent a rapid production of huge biomass with many ecological and economic consequences. However, their prediction remains elusive because the scale and dynamics of biomass accumulation are difficult to measure with sufficient spatial and temporal resolution to fully capture their magnitude. Lately, non-invasive biomass estimations based on unmanned aerial surveys of jellyfish distribution have been implemented. However, they generally occur at low altitudes and are not adequate to assess the entire spatial dimension of a jellyfish bloom. In this study, a high-resolution camera was on board of a small plane flying at 300 to 700 m altitude to map the Rhizostoma pulmo (Cnidaria; Rhizostomeae) bloom in an entire Mediterranean lagoon (38 km 2 ), at the beginning (July), the middle (August), and the end (October) of the 2022 event. Based on image analysis tailored algorithms we computed and mapped jellyfish densities and biomass for each flight. The maximum biomass was reached in July (886 t in total, maximum biomass density of 8 kg/100 m 2 ), followed by August (456 t, maximum biomass density of 3 kg/100 m 2 ). October displayed the least biomass (401 t, maximum biomass density of 3 kg/100 m 2 ). This approach provides novel insights of the R. pulmo bloom's spatial dimension and details on the jellyfish patchy distribution and temporal changes in population size structure, allowing detecting different cohorts. The aerial survey at high altitude proves to be an efficient method to monitor jellyfish blooms dynamics at coarse scale, although abundance and therefore biomass might be underestimated due to the individuals size detection limits. • Jellyfish patches were monitored using a camera on board of a small plane across Bages-Sigean lagoon covering the entire blooming season of Rhizostoma pulmo • A data-tailored algorithm was developed to detect R. pulmo bloom dynamics • Total biomass of R. pulmo during the bloom event was quantified from biomass distribution maps • Aerial surveys are an efficient non-invasive way to study spatio-temporal dynamics of medusae
Meffre et al. (Sun,) studied this question.