Transfer learning of species co-occurrence patterns between plant communities

8 Pág.

Detalles Bibliográficos
Autores: Hirn, Johannes, Sanz, Verónica, García, José Enrique, Goberna, Marta, Montesinos-Navarro, Alicia, Navarro-Cano, J. A., Sánchez-Martín, Ricardo, Valiente-Banuet, Alfonso, Verdú, Miguel
Tipo de recurso: artículo
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:10261/369173
Acceso en línea:http://hdl.handle.net/10261/369173
https://api.elsevier.com/content/abstract/scopus_id/85204779708
Access Level:acceso abierto
Palabra clave:Generative artificial intelligence
Patchy vegetation
Plant communities
Restoration ecology
Species co-occurrence
Variational autoencoders
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oai_identifier_str oai:digital.csic.es:10261/369173
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Transfer learning of species co-occurrence patterns between plant communities
title Transfer learning of species co-occurrence patterns between plant communities
spellingShingle Transfer learning of species co-occurrence patterns between plant communities
Hirn, Johannes
Generative artificial intelligence
Patchy vegetation
Plant communities
Restoration ecology
Species co-occurrence
Variational autoencoders
title_short Transfer learning of species co-occurrence patterns between plant communities
title_full Transfer learning of species co-occurrence patterns between plant communities
title_fullStr Transfer learning of species co-occurrence patterns between plant communities
title_full_unstemmed Transfer learning of species co-occurrence patterns between plant communities
title_sort Transfer learning of species co-occurrence patterns between plant communities
dc.creator.none.fl_str_mv Hirn, Johannes
Sanz, Verónica
García, José Enrique
Goberna, Marta
Montesinos-Navarro, Alicia
Navarro-Cano, J. A.
Sánchez-Martín, Ricardo
Valiente-Banuet, Alfonso
Verdú, Miguel
author Hirn, Johannes
author_facet Hirn, Johannes
Sanz, Verónica
García, José Enrique
Goberna, Marta
Montesinos-Navarro, Alicia
Navarro-Cano, J. A.
Sánchez-Martín, Ricardo
Valiente-Banuet, Alfonso
Verdú, Miguel
author_role author
author2 Sanz, Verónica
García, José Enrique
Goberna, Marta
Montesinos-Navarro, Alicia
Navarro-Cano, J. A.
Sánchez-Martín, Ricardo
Valiente-Banuet, Alfonso
Verdú, Miguel
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia e Innovación (España)
European Commission
Generalitat Valenciana
Hirn, Johannes [0000-0003-0267-2479]
Sanz, Verónica [0000-0001-8864-2507]
Goberna, M. [0000-0001-5303-3429]
Montesinos-Navarro, Alicia [0000-0003-4656-0321]
Navarro-Cano, J. A. [0000-0001-8091-1063]
Sánchez-Martín, Ricardo [0000-0001-5272-3276]
Valiente-Banuet, Alfonso [0000-0002-7533-6671]
Verdú, Miguel [0000-0002-9778-7692]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Generative artificial intelligence
Patchy vegetation
Plant communities
Restoration ecology
Species co-occurrence
Variational autoencoders
topic Generative artificial intelligence
Patchy vegetation
Plant communities
Restoration ecology
Species co-occurrence
Variational autoencoders
description 8 Pág.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
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/10261/369173
https://api.elsevier.com/content/abstract/scopus_id/85204779708
url http://hdl.handle.net/10261/369173
https://api.elsevier.com/content/abstract/scopus_id/85204779708
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113157GB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119634GB-I00
Departamento de Medio Ambiente y Agronomía​
https://doi.org/10.1016/j.ecoinf.2024.102826
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
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spelling Transfer learning of species co-occurrence patterns between plant communitiesHirn, JohannesSanz, VerónicaGarcía, José EnriqueGoberna, MartaMontesinos-Navarro, AliciaNavarro-Cano, J. A.Sánchez-Martín, RicardoValiente-Banuet, AlfonsoVerdú, MiguelGenerative artificial intelligencePatchy vegetationPlant communitiesRestoration ecologySpecies co-occurrenceVariational autoencoders8 Pág.Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and México. Time period: 2016–2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.Financial support was provided by the projects TED2021-129682B-I00, PID2020-113157GB-I00 (funded by MCIN/AEI/10.13039/501100011033 and ‘ERDF A way of making Europe’) and CIPROM/2021/63 (Generalitat Valenciana). JANC and MG also received funding through PID2020-119634GB-I00.Peer reviewedElsevierAgencia Estatal de Investigación (España)Ministerio de Ciencia e Innovación (España)European CommissionGeneralitat ValencianaHirn, Johannes [0000-0003-0267-2479]Sanz, Verónica [0000-0001-8864-2507]Goberna, M. [0000-0001-5303-3429]Montesinos-Navarro, Alicia [0000-0003-4656-0321]Navarro-Cano, J. A. [0000-0001-8091-1063]Sánchez-Martín, Ricardo [0000-0001-5272-3276]Valiente-Banuet, Alfonso [0000-0002-7533-6671]Verdú, Miguel [0000-0002-9778-7692]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/369173https://api.elsevier.com/content/abstract/scopus_id/85204779708reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/TED2021-129682B-I00/info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113157GB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119634GB-I00Departamento de Medio Ambiente y Agronomía​https://doi.org/10.1016/j.ecoinf.2024.102826Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3691732026-05-22T06:33:51Z
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