An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
| Autor: | Luciano Ortenzi, Javier Valencia, Sascha Flögel, Ander Zuazo, Vanesa López-Vázquez, Jordi Grinyó, Henning Wehde, Erik Rodriguez, Simone Marini, Guosong Zhang, Corrado Costa, Jacopo Aguzzi |
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| Přispěvatelé: | Norwegian Research Council, Federal Ministry of Economics and Technology (Germany), Helmholtz Association, European Commission, Agencia Estatal de Investigación (España) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Multivariate statistics multivariate statistics neural network Image processing filtering rhythms 02 engineering and technology Tides lcsh:Chemical technology 01 natural sciences Biochemistry Convolutional neural network Analytical Chemistry Observatory tides 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 14. Life underwater Electrical and Electronic Engineering Temporal scales Instrumentation Remote sensing deep-sea Artificial neural network 010604 marine biology & hydrobiology Filtering rhythms Deep-sea Automated video-imaging Atomic and Molecular Physics and Optics Neural network automated video imaging 13. Climate action Environmental science 020201 artificial intelligence & image processing Paragorgia arborea Cold-water corals Hydrography cold water coral (CWC) |
| Zdroj: | Sensors Volume 20 Issue 21 Sensors, Vol 20, Iss 6281, p 6281 (2020) Digital.CSIC. Repositorio Institucional del CSIC instname |
| ISSN: | 0010-8707 |
| DOI: | 10.3390/s20216281 |
| Popis: | 23 pages, 14 figures, 5 tables 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 particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures This project is funded by The Norwegian Research Council, Federal Ministry for Economic Affairs and Energy of Germany (03SX464C) and the Helmholtz Gemeinschaft Deutscher Forschungszentren (HGF) project Modular Observation Solutions for Earth Systems (MOSES), Spanish Centre for the Development of Industrial Technology (EXP 00108707/SERA-20181020), and co-funded by European Union’s Horizon 2020 research and innovation program under the framework of European Research Area Network (ERA-NET) Cofund Maritime and Marine Technologies for a new Era (MarTERA) With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) |
| Databáze: | OpenAIRE |
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