Sensors, Vol. 20, Pages 6281: An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Sensors, Vol. 20, Pages 6281: An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions Sensors doi: 10.3390/s20216281 Authors: Ander Zuazo Jordi Grinyó Vanesa López-Vázquez Erik Rodríguez Corrado Costa Luciano Ortenzi Sascha Flögel Javier Valencia Simone Marini Guosong Zhang Henning Wehde Jacopo Aguzzi Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in par...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research