Integrating diverse marine predator data for robust species distribution models in a dynamic ocean

[Data availability] Code and simulated sample data for running the analysis is available at: https://github.com/nfarchadi/BlueShark_ISDM/. Marker tag data used in this research are publicly available from the International Commission for the Conservation of Atlantic Tunas (ICCAT) Secretariat tag dat...

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Authors: Farchadi, Nima, Braun, Camrin D., Arostegui, Martin C., Lezama-Ochoa, Nerea, Pennino, Maria Grazia, Afonso, Pedro, Curtis, Tobey H., Fontes, Jorge, Queiroz, Nuno, Skomal, Gregory B., Sims, David W., Thorrold, Simon R., Vandeperre, Frederic, Lewison, Rebecca L.
Format: article
Status:Published version
Publication Date:2025
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/400894
Online Access:http://hdl.handle.net/10261/400894
https://api.elsevier.com/content/abstract/scopus_id/105010731929
Access Level:Open access
Keyword:Boosted regression trees
Data integration
Highly migratory species
Integrated nested Laplace approximation
Spatiotemporal dynamics
Species distribution models
id ES_bab46d16932d45809a2a78f0ffdcc40a
oai_identifier_str oai:digital.csic.es:10261/400894
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
title Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
spellingShingle Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
Farchadi, Nima
Boosted regression trees
Data integration
Highly migratory species
Integrated nested Laplace approximation
Spatiotemporal dynamics
Species distribution models
title_short Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
title_full Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
title_fullStr Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
title_full_unstemmed Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
title_sort Integrating diverse marine predator data for robust species distribution models in a dynamic ocean
dc.creator.none.fl_str_mv Farchadi, Nima
Braun, Camrin D.
Arostegui, Martin C.
Lezama-Ochoa, Nerea
Pennino, Maria Grazia
Afonso, Pedro
Curtis, Tobey H.
Fontes, Jorge
Queiroz, Nuno
Skomal, Gregory B.
Sims, David W.
Thorrold, Simon R.
Vandeperre, Frederic
Lewison, Rebecca L.
author Farchadi, Nima
author_facet Farchadi, Nima
Braun, Camrin D.
Arostegui, Martin C.
Lezama-Ochoa, Nerea
Pennino, Maria Grazia
Afonso, Pedro
Curtis, Tobey H.
Fontes, Jorge
Queiroz, Nuno
Skomal, Gregory B.
Sims, David W.
Thorrold, Simon R.
Vandeperre, Frederic
Lewison, Rebecca L.
author_role author
author2 Braun, Camrin D.
Arostegui, Martin C.
Lezama-Ochoa, Nerea
Pennino, Maria Grazia
Afonso, Pedro
Curtis, Tobey H.
Fontes, Jorge
Queiroz, Nuno
Skomal, Gregory B.
Sims, David W.
Thorrold, Simon R.
Vandeperre, Frederic
Lewison, Rebecca L.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NASA
National Oceanic and Atmospheric Administration (US)
Woods Hole Oceanographic Institution
Farchadi, Nima [0000-0003-4718-6984]
Braun, Camrin D. [0000-0002-9317-9489]
Arostegui, Martin C.[0000-0002-9313-9487]
Pennino, Maria Grazia [0000-0002-7577-2617]
Curtis, Tobey H. [0000-0003-0164-7335]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Boosted regression trees
Data integration
Highly migratory species
Integrated nested Laplace approximation
Spatiotemporal dynamics
Species distribution models
topic Boosted regression trees
Data integration
Highly migratory species
Integrated nested Laplace approximation
Spatiotemporal dynamics
Species distribution models
description [Data availability] Code and simulated sample data for running the analysis is available at: https://github.com/nfarchadi/BlueShark_ISDM/. Marker tag data used in this research are publicly available from the International Commission for the Conservation of Atlantic Tunas (ICCAT) Secretariat tag database at https://iccat.int/en/accesingdb.html. The fishery dependent observer dataset used in this study are considered confidential under the US Magnuson-Stevens Act. Qualified researchers may request these data from the NOAA Pelagic Observer Program office by contacting popobserver@noaa.gov. We requested data representing all pelagic longline sets between the years 1993 and 2019. For inquiries regarding the electronic data, contact the corresponding author.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
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/10261/400894
https://api.elsevier.com/content/abstract/scopus_id/105010731929
url http://hdl.handle.net/10261/400894
https://api.elsevier.com/content/abstract/scopus_id/105010731929
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1093/icesjms/fsaf110
https://doi.org/10.1093/icesjms/fsaf110
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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
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spelling Integrating diverse marine predator data for robust species distribution models in a dynamic oceanFarchadi, NimaBraun, Camrin D.Arostegui, Martin C.Lezama-Ochoa, NereaPennino, Maria GraziaAfonso, PedroCurtis, Tobey H.Fontes, JorgeQueiroz, NunoSkomal, Gregory B.Sims, David W.Thorrold, Simon R.Vandeperre, FredericLewison, Rebecca L.Boosted regression treesData integrationHighly migratory speciesIntegrated nested Laplace approximationSpatiotemporal dynamicsSpecies distribution models[Data availability] Code and simulated sample data for running the analysis is available at: https://github.com/nfarchadi/BlueShark_ISDM/. Marker tag data used in this research are publicly available from the International Commission for the Conservation of Atlantic Tunas (ICCAT) Secretariat tag database at https://iccat.int/en/accesingdb.html. The fishery dependent observer dataset used in this study are considered confidential under the US Magnuson-Stevens Act. Qualified researchers may request these data from the NOAA Pelagic Observer Program office by contacting popobserver@noaa.gov. We requested data representing all pelagic longline sets between the years 1993 and 2019. For inquiries regarding the electronic data, contact the corresponding author.Species distribution models (SDMs) are an important tool for marine conservation and management, yet guidance on leveraging diverse data to build robust models is limited. We evaluated whether an integrated SDM (iSDM) framework outperforms traditional data pooling or ensemble approaches when synthesizing multiple data types. We trained traditional SDMs and iSDMs using three data types for the blue shark (Prionace glauca) in the North Atlantic: fishery-dependent marker tags, observer records, and fishery-independent electronic tags. We compared pooled and ensembled SDMs, built with boosted regression trees, to an iSDM explicitly designed to address data-specific biases while leveraging each dataset’s strengths. While all approaches produced robust models, performance varied among data types, with fishery-dependent data consistently yielding more accurate than fishery-independent data. Differences in performance stemmed from models’ abilities to capture spatiotemporal dynamics in training data. iSDMs accounting for seasonal variability yielded the most accurate estimates but were computationally intensive, emphasizing the need to align model purpose with integration methods. Our findings reveal key trade-offs in data integration methods, particularly in balancing predictive accuracy and feasibility. As diverse data sources grow, leveraging robust approaches will be vital for improving conservation and management strategies and understanding dynamic species distributions in a changing ocean.This work was supported by a NASA Ecological Forecasting funded project (80NSSC19K0187) and N.F. was also support, in part, by the NOAA-Sea Grant Population and Ecosystem Dynamics Fellowship NA21OAR4170247. M.C.A. was supported by the WHOI President's Innovation Fund and C.D.B. was supported by the Robert L. James Early Career Scientist award at WHOI. P.A., F.V. and J.F. were supported by FCT through the IF/01640/2015; M3.1.a/F/062/201 and the strategic projects UIDB/05634/2020 and UIDP/05634/2020 .Peer reviewedOxford University PressNASANational Oceanic and Atmospheric Administration (US)Woods Hole Oceanographic InstitutionFarchadi, Nima [0000-0003-4718-6984]Braun, Camrin D. [0000-0002-9317-9489]Arostegui, Martin C.[0000-0002-9313-9487]Pennino, Maria Grazia [0000-0002-7577-2617]Curtis, Tobey H. [0000-0003-0164-7335]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/400894https://api.elsevier.com/content/abstract/scopus_id/105010731929reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1093/icesjms/fsaf110https://doi.org/10.1093/icesjms/fsaf110Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4008942026-05-22T06:33:51Z
score 15.81155

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