Abstract
Barbed wire jellyfish (Apolemia sp.) is a marine organism that self-organizes into string-like colonies of length ranging from a few centimetres to over 30 metres. The individuals that make up a colony contain hair-like structures with stinging cells – making a colony resemble barbed wire both in look and touch. The genus is distributed in the Mediterranean, Pacific, and Atlantic seas. In recent years, it has been observed with great abundance along the Norwegian coast, both intact colonies and swarms of fragments. Such swarms have caused significant disruption of aquaculture operations and mass mortality events in salmon fish farms, resulting in economic losses.
The new appearance of barbed wire jellyfish along the Norwegian coast, and the ensuing challenges for Norwegian aquaculture, necessitate a better understanding of their distribution and abundance. However, these gelatinous zooplankton (such as Apolemia sp.) are often overlooked in Norwegian marine monitoring programs. Moreover, traditional sampling techniques are poorly suited for capturing gelatinous organisms, leading to significant knowledge gaps regarding their distribution, abundance, and seasonal dynamics across Norwegian waters. To address these challenges, the Norwegian citizen science portal “Dugnad for Havet” (DfH) was launched in 2019.
Using expert-validated images from DfH, we have developed and benchmarked machine learning models which detect barbed wire jellyfish in citizen science images. We describe these models and their performance below. The best-performing model is intended for integration into DfH to automate identification of Apolemia sp. and is part of the development of an early-warning system through the JellySafe project.