A deep Generative Artificial Intelligence system to predict species coexistence patterns

Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge...

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Detalles Bibliográficos
Autores: Hirn, Johannes, García, José Enrique, Montesinos-Navarro, Alicia, Sánchez-Martín, Ricardo, Sanz, Verónica, Verdú, Miguel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/284176
Acceso en línea:http://hdl.handle.net/10261/284176
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Direct interactions
Generative adversarial networks
Indirect interactions
Species coexistence
Variational AutoEncoders
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spelling A deep Generative Artificial Intelligence system to predict species coexistence patternsHirn, JohannesGarcía, José EnriqueMontesinos-Navarro, AliciaSánchez-Martín, RicardoSanz, VerónicaVerdú, MiguelArtificial intelligenceDirect interactionsGenerative adversarial networksIndirect interactionsSpecies coexistenceVariational AutoEncodersPredicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.The authors thank the Yesaires team for making the fieldwork of quantification of species gypsum affinity possible. R.S.-M. was supported by the Ministry of Science and Innovations (FPU grant FPU17/00629). Financial support was provided by the projects RTI2018-099672-J-I00 and PID2020-113157GB-I00 (funded by MCIN/AEI/10.13039/501100011033 and ‘ERDF A way of making Europe’).British Ecological SocietyMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/284176reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099672-J-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113157GB-I00http://dx.doi.org/10.1111/2041-210X.13827Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2841762026-05-22T06:33:51Z
dc.title.none.fl_str_mv A deep Generative Artificial Intelligence system to predict species coexistence patterns
title A deep Generative Artificial Intelligence system to predict species coexistence patterns
spellingShingle A deep Generative Artificial Intelligence system to predict species coexistence patterns
Hirn, Johannes
Artificial intelligence
Direct interactions
Generative adversarial networks
Indirect interactions
Species coexistence
Variational AutoEncoders
title_short A deep Generative Artificial Intelligence system to predict species coexistence patterns
title_full A deep Generative Artificial Intelligence system to predict species coexistence patterns
title_fullStr A deep Generative Artificial Intelligence system to predict species coexistence patterns
title_full_unstemmed A deep Generative Artificial Intelligence system to predict species coexistence patterns
title_sort A deep Generative Artificial Intelligence system to predict species coexistence patterns
dc.creator.none.fl_str_mv Hirn, Johannes
García, José Enrique
Montesinos-Navarro, Alicia
Sánchez-Martín, Ricardo
Sanz, Verónica
Verdú, Miguel
author Hirn, Johannes
author_facet Hirn, Johannes
García, José Enrique
Montesinos-Navarro, Alicia
Sánchez-Martín, Ricardo
Sanz, Verónica
Verdú, Miguel
author_role author
author2 García, José Enrique
Montesinos-Navarro, Alicia
Sánchez-Martín, Ricardo
Sanz, Verónica
Verdú, Miguel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Artificial intelligence
Direct interactions
Generative adversarial networks
Indirect interactions
Species coexistence
Variational AutoEncoders
topic Artificial intelligence
Direct interactions
Generative adversarial networks
Indirect interactions
Species coexistence
Variational AutoEncoders
description Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
2022
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/10261/284176
url http://hdl.handle.net/10261/284176
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099672-J-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113157GB-I00
http://dx.doi.org/10.1111/2041-210X.13827
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 British Ecological Society
publisher.none.fl_str_mv British Ecological Society
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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