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Unraveling the Microbiome–Environmental Change Nexus to Contribute to a More Sustainable World: A Comprehensive Review of Artificial Intelligence Approaches

Author

Listed:
  • Maria Inês Barbosa

    (CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal)

  • Gabriel Silva

    (CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
    CITAR—Centro de Investigação em Ciência e Tecnologia das Artes, Escola das Artes, Universidade Católica Portuguesa, 4169-005 Porto, Portugal)

  • Pedro Ribeiro

    (CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal)

  • Eduarda Vieira

    (CITAR—Centro de Investigação em Ciência e Tecnologia das Artes, Escola das Artes, Universidade Católica Portuguesa, 4169-005 Porto, Portugal)

  • André Perrotta

    (Centre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal)

  • Patrícia Moreira

    (CITAR—Centro de Investigação em Ciência e Tecnologia das Artes, Escola das Artes, Universidade Católica Portuguesa, 4169-005 Porto, Portugal)

  • Pedro Miguel Rodrigues

    (CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal)

Abstract

This review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability.

Suggested Citation

  • Maria Inês Barbosa & Gabriel Silva & Pedro Ribeiro & Eduarda Vieira & André Perrotta & Patrícia Moreira & Pedro Miguel Rodrigues, 2025. "Unraveling the Microbiome–Environmental Change Nexus to Contribute to a More Sustainable World: A Comprehensive Review of Artificial Intelligence Approaches," Sustainability, MDPI, vol. 17(16), pages 1-32, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7209-:d:1720990
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    References listed on IDEAS

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    1. Laura Recuero Virto, 2018. "A preliminary assessment of the indicators for Sustainable Development Goal (SDG) 14 “Conserve and sustainably use the oceans, seas and marine resources for sustainable development”," Policy Papers 2018.03, FAERE - French Association of Environmental and Resource Economists.
    2. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Jaron Thompson & Renee Johansen & John Dunbar & Brian Munsky, 2019. "Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-16, July.
    5. Dan Song & Tangbin Huo & Zhao Zhang & Lei Cheng & Le Wang & Kun Ming & Hui Liu & Mengsha Li & Xue Du, 2022. "Metagenomic Analysis Reveals the Response of Microbial Communities and Their Functions in Lake Sediment to Environmental Factors," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
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