Effects of cloud-to-water lightning strokes on open sea fish cages during eastern Mediterranean winter thunderstorms

Based on data obtained by the Earth Networks Total Lightning Network (ENTLN) for 5 winter seasons (DJF, 2018-2022), the flash density of lightning striking the water surface of the eastern Mediterranean Sea up to 50 km from the Israeli coastline is on average 3 strokes/km². Out of the total lightning that strike the sea surface in the said area, about 0.05% on are superbolts with peak current > 200 kA. Cloud-to-water strikes generate thunder and underwater acoustic noise that can propagate for a few km from the strike location. While anthropogenic noises have been shown to cause negative stress responses in the marine environment and specifically in aquaculture fish cages, no stress response of cultured fish due to lightning strikes have been recorded yet.  New areas in the Israeli territorial waters are allocated to fish farms. These commercial farms will be using net cages, with high fish density expecting large yields.

This research aims to find out how cultured fish respond to the acoustic noises generated by lightning strikes. This hypothesis meets a growing awareness in the aquaculture field to research fish stress that, in this case, stay trapped in the water body without the ability to effectively respond and flee lightning strikes. Continual stress of cultured fish can economically adversely affect the fish farm due to high mortality rates and decreased growth rates. By monitoring sea bream (Sparus aurata) cages, with cameras and hydrophone, during winter months of years 2021-2023, we have found several cases of stress related behavior. These cases were correlated with precise lightnings data, videos of surveillance cameras pointed toward the fish farm, audio records of underwater sound and indications of abnormal fish behavior (sudden dive or direction changes). We will present results from newly developed image processing algorithm that reads underwater fish videos files and automatically finds abnormal behavior events.