New ecological knowledge of the bubblegum coral Paragorgia arborea (L. 1758) using computer vision

Modern stationary observatories, equipped with cameras and other sensors, make it possible to observe and study cold-water corals in situ over increasingly long periods of time. In this study we investigate data collected for a bubblegum coral (Paragorgia arborea) with the LoVe (Lofoten Vesteralen) observatory for a period of 117 days, including time series of images, current, water depth and temperature. This kind of multimodal time series data represent a new methodological challenge for marine technology and bioinformatics. In this work, we continue on the path to computer-aided ocean data analysis and show how, in addition to polyp activity, morphological parameters can be extracted from image data with a new deep learning-based approach. Using customized visualizations we show how this image-derived data together with the environmental data can be analysed and new interpretations are supported. Our results indicate that the polyp activity and branch diameter variation followed a semi-cyclic pattern, with the polyps changing from fully retracted to fully expanded faster than the image sampling rate, resulting in an almost binary time series that showed positive correlation with the current strength, which may be related to food supply. The periods of full polyp expansion ranged from 1 to 4 h, with the majority around 1 h. The variation in branch diameter and volume of intra-skeletal channels probably relates to the intra-colonial transport of nutrients and waste products. During a 16 day period in March 2019, the polyps remained retracted for an extended period accompanied by little variation in branch diameter. This behaviour was not related to any of the measured environmental conditions. Further studies are required to determine if this could be linked to biological processes, such as reproduction.