Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review
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...
| Main Authors: | , , , , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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Elsevier
2025
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| Online Access: | 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 |
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| author | Martinez, Emilce Soledad Tejada-Gutiérrez, Eva Sorribas, Albert Mateo-Fornes, Jordi Solsona, Francesc Defacio, Raquel Alicia Alves, Rui |
| author_browse | Alves, Rui Defacio, Raquel Alicia Martinez, Emilce Soledad Mateo-Fornes, Jordi Solsona, Francesc Sorribas, Albert Tejada-Gutiérrez, Eva |
| author_facet | Martinez, Emilce Soledad Tejada-Gutiérrez, Eva Sorribas, Albert Mateo-Fornes, Jordi Solsona, Francesc Defacio, Raquel Alicia Alves, Rui |
| author_sort | Martinez, Emilce Soledad |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA24480 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA244802025-11-06T10:41:30Z Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review Martinez, Emilce Soledad Tejada-Gutiérrez, Eva Sorribas, Albert Mateo-Fornes, Jordi Solsona, Francesc Defacio, Raquel Alicia Alves, Rui Biodiversidad Base de Datos Integración Ecología Biodiversity Databases Integration Ecology Multimodal Data Data Integration Big Data Smart Data Mathematical Ecology 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 2025-11-06T10:37:29Z 2025-11-06T10:37:29Z 2025-12 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf Elsevier Ecological Informatics 92 : 103485. (December 2025) |
| spellingShingle | Biodiversidad Base de Datos Integración Ecología Biodiversity Databases Integration Ecology Multimodal Data Data Integration Big Data Smart Data Mathematical Ecology Martinez, Emilce Soledad Tejada-Gutiérrez, Eva Sorribas, Albert Mateo-Fornes, Jordi Solsona, Francesc Defacio, Raquel Alicia Alves, Rui 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 |
| 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_short | 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 |
| topic | Biodiversidad Base de Datos Integración Ecología Biodiversity Databases Integration Ecology Multimodal Data Data Integration Big Data Smart Data Mathematical Ecology |
| 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 |
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