Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]

Vinha, Beatriz; Murillo, Francisco Javier; Schumacher, Mia; Hansteen, Thor H.; Schwarzkopf, Franziska U.; Biastoch, Arne; Kenchington, Ellen; Piraino, Stefano; Orejas, Covadonga; Huvenne, Veerle A.I.

Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]

Aim: Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the...

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Autores:	Vinha, Beatriz, Murillo, Francisco Javier, Schumacher, Mia, Hansteen, Thor H., Schwarzkopf, Franziska U., Biastoch, Arne, Kenchington, Ellen, Piraino, Stefano, Orejas, Covadonga, Huvenne, Veerle A.I.
Tipo de recurso:	conjunto de datos
Estado:	Versión publicada
Fecha de publicación:	2024
País:	España
Institución:	Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:	DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:	oai:digital.csic.es:10261/386142
Acceso en línea:	http://hdl.handle.net/10261/386142
Access Level:	acceso abierto
Palabra clave:	Seamounts Earth and related environmental sciences Cabo Verde Cold-water corals Deep-sea ecosystems Ensemble modelling Species distribution models Vulnerable Marine Ecosystems

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oai_identifier_str	oai:digital.csic.es:10261/386142
network_acronym_str	ES
network_name_str	España
repository_id_str
dc.title.none.fl_str_mv	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
title	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
spellingShingle	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset] Vinha, Beatriz Seamounts Earth and related environmental sciences Cabo Verde Cold-water corals Deep-sea ecosystems Ensemble modelling Species distribution models Vulnerable Marine Ecosystems
title_short	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
title_full	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
title_fullStr	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
title_full_unstemmed	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
title_sort	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]
dc.creator.none.fl_str_mv	Vinha, Beatriz Murillo, Francisco Javier Schumacher, Mia Hansteen, Thor H. Schwarzkopf, Franziska U. Biastoch, Arne Kenchington, Ellen Piraino, Stefano Orejas, Covadonga Huvenne, Veerle A.I.
author	Vinha, Beatriz
author_facet	Vinha, Beatriz Murillo, Francisco Javier Schumacher, Mia Hansteen, Thor H. Schwarzkopf, Franziska U. Biastoch, Arne Kenchington, Ellen Piraino, Stefano Orejas, Covadonga Huvenne, Veerle A.I.
author_role	author
author2	Murillo, Francisco Javier Schumacher, Mia Hansteen, Thor H. Schwarzkopf, Franziska U. Biastoch, Arne Kenchington, Ellen Piraino, Stefano Orejas, Covadonga Huvenne, Veerle A.I.
author2_role	author author author author author author author author author
dc.contributor.none.fl_str_mv	European Commission Vinha, Beatriz [0000-0001-7193-8387] Orejas, Covadonga [0000-0002-2580-1002] Kenchington, Ellen [0000-0003-3784-4533] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv	Seamounts Earth and related environmental sciences Cabo Verde Cold-water corals Deep-sea ecosystems Ensemble modelling Species distribution models Vulnerable Marine Ecosystems
topic	Seamounts Earth and related environmental sciences Cabo Verde Cold-water corals Deep-sea ecosystems Ensemble modelling Species distribution models Vulnerable Marine Ecosystems
description	Aim: Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.
publishDate	2024
dc.date.none.fl_str_mv	2024 2025 2025
dc.type.none.fl_str_mv	info:eu-repo/semantics/dataset http://purl.org/coar/resource_type/c_ddb1 Publisher's version info:eu-repo/semantics/publishedVersion
format	dataset
status_str	publishedVersion
dc.identifier.none.fl_str_mv	http://hdl.handle.net/10261/386142
url	http://hdl.handle.net/10261/386142
dc.language.none.fl_str_mv	Inglés
language_invalid_str_mv	Inglés
dc.relation.none.fl_str_mv	#PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/H2020/ 818123 Vinha, Beatriz; Murillo, Francisco Javier; Schumacher, Mia; Hansteen, Thor H.; Schwarzkopf, Franziska U.; Biastoch, Arne; Kenchington, Ellen; Piraino, Stefano; Orejas, Covadonga; Huvenne, Veerle A.I. 2024. Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa). https://doi.org/10.1111/ddi.13896. http://hdl.handle.net/10261/386106 Vinha, Beatriz; Hansteen, Thor H.; Huvenne, Veerle A.I.; Orejas, Covadonga; 2023; Presence-absence records for four cold-water coral taxa on the seamounts of Cabo Verde (NW Africa) [Dataset]; PANGAEA; https://doi.org/10.1594/PANGAEA.963704 https://doi.org/10.5061/dryad.0vt4b8h5g Sí
dc.rights.none.fl_str_mv	info:eu-repo/semantics/openAccess
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	text/csv
dc.publisher.none.fl_str_mv	Dryad
publisher.none.fl_str_mv	Dryad
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_	1869405332534460416
spelling	Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]Vinha, BeatrizMurillo, Francisco JavierSchumacher, MiaHansteen, Thor H.Schwarzkopf, Franziska U.Biastoch, ArneKenchington, EllenPiraino, StefanoOrejas, CovadongaHuvenne, Veerle A.I.SeamountsEarth and related environmental sciencesCabo VerdeCold-water coralsDeep-sea ecosystemsEnsemble modellingSpecies distribution modelsVulnerable Marine EcosystemsAim: Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce.Location: Seamounts around the Cabo Verde Archipelago (NW Africa).Methods: We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation.Results: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.Main conclusions: Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.Methods. Terrain variables were derived from a 100 m resolution bathymetry grid, created from a compilation of all available bathymetry data collected by multibeam echosounder (MBES) in the Cabo Verde region. We used an analytical multiscale approach to calculate terrain variables by considering, when possible, different neighbourhood sizes (i.e., number of grid-cells (n)) for calculations. In this study, slope, aspect (converted to eastness and northness), and three types of terrain curvature (plan, profile and mean) were calculated following a Fibonacci sequence of four increasing n values (n = 3, 9, 17, 33) (Dolan et al., 2008). For this, the functions ‘SlpAsp’ and ‘Qfit’ of the “Multiscale DTM” library (Ilich et al., 2023) were used in R Studio. Topographic Position Index (TPI) and Vector Ruggedness Measure (VRM) were calculated at two scales, both fine- and broad-scales (n = 3, 33), using the ‘tpi’ and ‘vrm’ functions, respectively, of the “spatialEco” R Package (Evans and Ram, 2021). Roughness and Terrain Ruggedness Index (TRI) were calculated using the ‘terrain’ function from the “raster” R package (Hijmans et al., 2015), using the default n = 3. Final terrain variables and scales considered in the models were chosen after investigating collinearity between variables (see next section on initial variable selection). The monthly averages of bottom temperature, bottom salinity and bottom zonal (U) and meridional (V) velocity components for the period of 2009 to 2019 were obtained from a hindcast simulation in the high-resolution VIKING20X ocean general circulation model (VIKING20X-JRA-OMIP described in Biastoch et al., 2021), with a native horizontal resolution of 1/20° (~ 5.3 km). Bottom U and V were converted into mean bottom current speed.European Union : 818123, Horizon 2020Peer reviewedDryadEuropean CommissionVinha, Beatriz [0000-0001-7193-8387]Orejas, Covadonga [0000-0002-2580-1002]Kenchington, Ellen [0000-0003-3784-4533]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1Publisher's versioninfo:eu-repo/semantics/publishedVersiontext/csvhttp://hdl.handle.net/10261/386142reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/ 818123Vinha, Beatriz; Murillo, Francisco Javier; Schumacher, Mia; Hansteen, Thor H.; Schwarzkopf, Franziska U.; Biastoch, Arne; Kenchington, Ellen; Piraino, Stefano; Orejas, Covadonga; Huvenne, Veerle A.I. 2024. Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa). https://doi.org/10.1111/ddi.13896. http://hdl.handle.net/10261/386106Vinha, Beatriz; Hansteen, Thor H.; Huvenne, Veerle A.I.; Orejas, Covadonga; 2023; Presence-absence records for four cold-water coral taxa on the seamounts of Cabo Verde (NW Africa) [Dataset]; PANGAEA; https://doi.org/10.1594/PANGAEA.963704https://doi.org/10.5061/dryad.0vt4b8h5gSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3861422026-05-22T06:33:51Z
score	15,811543

Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [Dataset]

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