Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review

Autores
Martinez, Emilce Soledad; Tejada-Gutiérrez, Eva; Sorribas, Albert; Mateo-Fornes, Jordi; Solsona, Francesc; Defacio, Raquel Alicia; Alves, Rui
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The integration of multimodal data to analyze, model, and predict changes in plant biodiversity is critical for addressing global conservation challenges. This systematic review examines the current landscape of plant biodiversity data, focusing on the identification, classification, and evaluation of key open-access data sources and integration methodologies. We highlight the strengths and limitations of major biodiversity platforms, emphasizing their contributions to species occurrence, trait data, taxonomic checklists, and environmental variables. The review also explores computational tools for data integration. We describe and analyze the role of Darwin Core standards in data standardization, harmonization, and interoperability, highlighting the importance of tools such as Species Distribution Models and machine learning. Additionally, we assess the tools available for multimodal data integration and analysis of the effects of environmental drivers (e.g., temperature, precipitation, topography) on biodiversity. We find significant advancements in biodiversity informatics over the last decades. Still, challenges persist in achieving interoperability across datasets, in addressing spatial and temporal biases, and in integrating remote sensing with in situ observations. By identifying both the challenges and emerging solutions, this review contributes to advancing biodiversity monitoring strategies, aligning with global conservation goals outlined by the Convention on Biological Diversity and the United Nations Sustainable Development Goal 15. Ultimately, the findings underscore the importance of harmonized data integration frameworks to enhance predictive modeling capabilities and inform effective conservation policies.
EEA Pergamino
Fil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; Argentina
Fil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Tejada-Gutiérrez, Eva. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Mateo-Fornes, Jordi. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España
Fil: Solsona, Francesc. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España
Fil: Defacio, Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Recursos Genéticos; Argentina
Fil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fuente
Ecological Informatics 92 : 103485. (December 2025)
Materia
Biodiversidad
Base de Datos
Integración
Ecología
Biodiversity
Databases
Integration
Ecology
Multimodal Data
Data Integration
Big Data
Smart Data
Mathematical Ecology
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/24480
Acceder
id INTADig_17d7e8280d8533c12355e698cfea806b
oai_identifier_str oai:localhost:20.500.12123/24480
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic reviewMartinez, Emilce SoledadTejada-Gutiérrez, EvaSorribas, AlbertMateo-Fornes, JordiSolsona, FrancescDefacio, Raquel AliciaAlves, RuiBiodiversidadBase de DatosIntegraciónEcologíaBiodiversityDatabasesIntegrationEcologyMultimodal DataData IntegrationBig DataSmart DataMathematical EcologyThe integration of multimodal data to analyze, model, and predict changes in plant biodiversity is critical for addressing global conservation challenges. This systematic review examines the current landscape of plant biodiversity data, focusing on the identification, classification, and evaluation of key open-access data sources and integration methodologies. We highlight the strengths and limitations of major biodiversity platforms, emphasizing their contributions to species occurrence, trait data, taxonomic checklists, and environmental variables. The review also explores computational tools for data integration. We describe and analyze the role of Darwin Core standards in data standardization, harmonization, and interoperability, highlighting the importance of tools such as Species Distribution Models and machine learning. Additionally, we assess the tools available for multimodal data integration and analysis of the effects of environmental drivers (e.g., temperature, precipitation, topography) on biodiversity. We find significant advancements in biodiversity informatics over the last decades. Still, challenges persist in achieving interoperability across datasets, in addressing spatial and temporal biases, and in integrating remote sensing with in situ observations. By identifying both the challenges and emerging solutions, this review contributes to advancing biodiversity monitoring strategies, aligning with global conservation goals outlined by the Convention on Biological Diversity and the United Nations Sustainable Development Goal 15. Ultimately, the findings underscore the importance of harmonized data integration frameworks to enhance predictive modeling capabilities and inform effective conservation policies.EEA PergaminoFil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; ArgentinaFil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Tejada-Gutiérrez, Eva. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Mateo-Fornes, Jordi. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; EspañaFil: Solsona, Francesc. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; EspañaFil: Defacio, Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Recursos Genéticos; ArgentinaFil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaElsevier2025-11-06T10:37:29Z2025-11-06T10:37:29Z2025-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/24480https://www.sciencedirect.com/science/article/pii/S15749541250049471574-9541https://doi.org/10.1016/j.ecoinf.2025.103485Ecological Informatics 92 : 103485. (December 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-11-13T08:48:48Zoai:localhost:20.500.12123/24480instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-11-13 08:48:48.596INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
title Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
spellingShingle Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
Martinez, Emilce Soledad
Biodiversidad
Base de Datos
Integración
Ecología
Biodiversity
Databases
Integration
Ecology
Multimodal Data
Data Integration
Big Data
Smart Data
Mathematical Ecology
title_short Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
title_full Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
title_fullStr Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
title_full_unstemmed Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
title_sort Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
dc.creator.none.fl_str_mv Martinez, Emilce Soledad
Tejada-Gutiérrez, Eva
Sorribas, Albert
Mateo-Fornes, Jordi
Solsona, Francesc
Defacio, Raquel Alicia
Alves, Rui
author Martinez, Emilce Soledad
author_facet Martinez, Emilce Soledad
Tejada-Gutiérrez, Eva
Sorribas, Albert
Mateo-Fornes, Jordi
Solsona, Francesc
Defacio, Raquel Alicia
Alves, Rui
author_role author
author2 Tejada-Gutiérrez, Eva
Sorribas, Albert
Mateo-Fornes, Jordi
Solsona, Francesc
Defacio, Raquel Alicia
Alves, Rui
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Biodiversidad
Base de Datos
Integración
Ecología
Biodiversity
Databases
Integration
Ecology
Multimodal Data
Data Integration
Big Data
Smart Data
Mathematical Ecology
topic Biodiversidad
Base de Datos
Integración
Ecología
Biodiversity
Databases
Integration
Ecology
Multimodal Data
Data Integration
Big Data
Smart Data
Mathematical Ecology
dc.description.none.fl_txt_mv The integration of multimodal data to analyze, model, and predict changes in plant biodiversity is critical for addressing global conservation challenges. This systematic review examines the current landscape of plant biodiversity data, focusing on the identification, classification, and evaluation of key open-access data sources and integration methodologies. We highlight the strengths and limitations of major biodiversity platforms, emphasizing their contributions to species occurrence, trait data, taxonomic checklists, and environmental variables. The review also explores computational tools for data integration. We describe and analyze the role of Darwin Core standards in data standardization, harmonization, and interoperability, highlighting the importance of tools such as Species Distribution Models and machine learning. Additionally, we assess the tools available for multimodal data integration and analysis of the effects of environmental drivers (e.g., temperature, precipitation, topography) on biodiversity. We find significant advancements in biodiversity informatics over the last decades. Still, challenges persist in achieving interoperability across datasets, in addressing spatial and temporal biases, and in integrating remote sensing with in situ observations. By identifying both the challenges and emerging solutions, this review contributes to advancing biodiversity monitoring strategies, aligning with global conservation goals outlined by the Convention on Biological Diversity and the United Nations Sustainable Development Goal 15. Ultimately, the findings underscore the importance of harmonized data integration frameworks to enhance predictive modeling capabilities and inform effective conservation policies.
EEA Pergamino
Fil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; Argentina
Fil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Tejada-Gutiérrez, Eva. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Mateo-Fornes, Jordi. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España
Fil: Solsona, Francesc. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España
Fil: Defacio, Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Recursos Genéticos; Argentina
Fil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
description The integration of multimodal data to analyze, model, and predict changes in plant biodiversity is critical for addressing global conservation challenges. This systematic review examines the current landscape of plant biodiversity data, focusing on the identification, classification, and evaluation of key open-access data sources and integration methodologies. We highlight the strengths and limitations of major biodiversity platforms, emphasizing their contributions to species occurrence, trait data, taxonomic checklists, and environmental variables. The review also explores computational tools for data integration. We describe and analyze the role of Darwin Core standards in data standardization, harmonization, and interoperability, highlighting the importance of tools such as Species Distribution Models and machine learning. Additionally, we assess the tools available for multimodal data integration and analysis of the effects of environmental drivers (e.g., temperature, precipitation, topography) on biodiversity. We find significant advancements in biodiversity informatics over the last decades. Still, challenges persist in achieving interoperability across datasets, in addressing spatial and temporal biases, and in integrating remote sensing with in situ observations. By identifying both the challenges and emerging solutions, this review contributes to advancing biodiversity monitoring strategies, aligning with global conservation goals outlined by the Convention on Biological Diversity and the United Nations Sustainable Development Goal 15. Ultimately, the findings underscore the importance of harmonized data integration frameworks to enhance predictive modeling capabilities and inform effective conservation policies.
publishDate 2025
dc.date.none.fl_str_mv 2025-11-06T10:37:29Z
2025-11-06T10:37:29Z
2025-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/24480
https://www.sciencedirect.com/science/article/pii/S1574954125004947
1574-9541
https://doi.org/10.1016/j.ecoinf.2025.103485
url http://hdl.handle.net/20.500.12123/24480
https://www.sciencedirect.com/science/article/pii/S1574954125004947
https://doi.org/10.1016/j.ecoinf.2025.103485
identifier_str_mv 1574-9541
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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 Ecological Informatics 92 : 103485. (December 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1848680557562560512
score 13.24909

Publicaciones similares