Assessing marine litter on the VMEs of el Seco de los Olivos (W Mediterranean Sea) [Dataset]

Marine litter observations on Vulnerable Marine Ecosystems of Chella bank (Alboran Sea), which is part of the Sur de Almería - El Seco de los Olivos Site of Community importance of the Natura 2000 Network. This data was obtained through the analyses of 55 ROV transects from three consecutive OCEANA...

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Detalles Bibliográficos
Autores: Martínez-Dios, Ariadna, Foglini, Federica, González-Irusta, José Manuel, Serrano López, Alberto, Lo Iacono, Claudio, De-la-Torriente, Ana
Tipo de recurso: conjunto de datos
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/392390
Acceso en línea:http://hdl.handle.net/10261/392390
Access Level:acceso abierto
Palabra clave:Marine pollution
Mediterranean sea
Deep sea fishing
Benthic ecosystem
Descripción
Sumario:Marine litter observations on Vulnerable Marine Ecosystems of Chella bank (Alboran Sea), which is part of the Sur de Almería - El Seco de los Olivos Site of Community importance of the Natura 2000 Network. This data was obtained through the analyses of 55 ROV transects from three consecutive OCEANA expeditions. All human-derived items observed in the videos were identified, counted, and classified according to the joint list of litter categories developed in the context of the EU Marine Strategy Framework Directive in collaboration with Regional Sea Conventions (RSCs) (Fleet et al., 2020) and according to its nature (either ALDFG, Nautical, Urban and Military). In addition, all impacted organisms were identified to the lowest possible taxonomic level and manually annotated. In absence of a standardized framework for reporting marine litter-fauna interactions (Bruemmer et al., 2023), we followed the classification proposed by de Carvalho-Souza et al. (2018). Biogenic habitats and substrate type were also evaluated directly from the video footage based on categories defined by De la Torriente et al. (2018, 2019).