An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

23 pages, 14 figures, 5 tables

Detalhes bibliográficos
Autores: Zuazo, Ander, Grinyó, Jordi, López-Vázquez, Vanesa, Rodriguez, Erik, Costa, Corrado, Flögel, Sascha, Valencia, Javier, Marini, Simone, Zhang, Guosong, Wehde, Henning, Aguzzi, Jacopo
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/223108
Acesso em linha:http://hdl.handle.net/10261/223108
Access Level:acceso abierto
Palavra-chave:Neural networks
Deep-sea
Cold-water corals
Automated video-imaging
Filtering rhythms
Tides
Multivariate statistics
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spelling An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic ConditionsZuazo, AnderGrinyó, JordiLópez-Vázquez, VanesaRodriguez, ErikCosta, CorradoFlögel, SaschaValencia, JavierMarini, SimoneZhang, GuosongWehde, HenningAguzzi, JacopoNeural networksDeep-seaCold-water coralsAutomated video-imagingFiltering rhythmsTidesMultivariate statistics23 pages, 14 figures, 5 tablesImaging 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 infrastructuresThis 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)Molecular Diversity Preservation InternationalNorwegian Research CouncilFederal Ministry of Economics and Technology (Germany)Helmholtz AssociationEuropean CommissionAgencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020202020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/223108reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#nfo:eu-repo/grantAgreement/EC/H2020/728053https://doi.org/10.3390/s20216281Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2231082026-05-22T06:33:51Z
dc.title.none.fl_str_mv An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
spellingShingle An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
Zuazo, Ander
Neural networks
Deep-sea
Cold-water corals
Automated video-imaging
Filtering rhythms
Tides
Multivariate statistics
title_short An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_full An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_fullStr An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_full_unstemmed An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_sort An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
dc.creator.none.fl_str_mv Zuazo, Ander
Grinyó, Jordi
López-Vázquez, Vanesa
Rodriguez, Erik
Costa, Corrado
Flögel, Sascha
Valencia, Javier
Marini, Simone
Zhang, Guosong
Wehde, Henning
Aguzzi, Jacopo
author Zuazo, Ander
author_facet Zuazo, Ander
Grinyó, Jordi
López-Vázquez, Vanesa
Rodriguez, Erik
Costa, Corrado
Flögel, Sascha
Valencia, Javier
Marini, Simone
Zhang, Guosong
Wehde, Henning
Aguzzi, Jacopo
author_role author
author2 Grinyó, Jordi
López-Vázquez, Vanesa
Rodriguez, Erik
Costa, Corrado
Flögel, Sascha
Valencia, Javier
Marini, Simone
Zhang, Guosong
Wehde, Henning
Aguzzi, Jacopo
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Norwegian Research Council
Federal Ministry of Economics and Technology (Germany)
Helmholtz Association
European Commission
Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Neural networks
Deep-sea
Cold-water corals
Automated video-imaging
Filtering rhythms
Tides
Multivariate statistics
topic Neural networks
Deep-sea
Cold-water corals
Automated video-imaging
Filtering rhythms
Tides
Multivariate statistics
description 23 pages, 14 figures, 5 tables
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/223108
url http://hdl.handle.net/10261/223108
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
nfo:eu-repo/grantAgreement/EC/H2020/728053
https://doi.org/10.3390/s20216281
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406196511801344
score 15,812455

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