| Title: | Machine learning-driven mapping of prokaryotic community diversity in the Mediterranean Sea using omics, earth observation, and model data |
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| Authors: | ID Marchese, Christian (Author)
ID Zoffoli, Maria Laura (Author)
ID Ramond, Pierre (Author)
ID Tinta, Tinkara (Author)
ID Orel, Neža (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S1574954126001536?via%3Dihub
 PDF - Presentation file, download (11,44 MB)
MD5: F65E2CC59EF108B0338FC4C804386D81
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  NIB - National Institute of Biology
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| Abstract: | Marine prokaryotic communities are major contributors to oceanic food webs and global biogeochemical cycles. However, basin-scale diversity patterns and environmental drivers remain poorly understood. In this study, we applied a machine-learning framework to model the diversity of marine prokaryotic communities across the Mediterranean Sea. Diversity was quantified using the Shannon Diversity Index (SDI) derived from 16S rRNA gene sequencing. The in situ dataset included ~600 samples collected year-round from 2001 to 2023 at coastal and open-water sites, providing broad temporal coverage and multisite spatial sampling. We trained an XGBoost model using satellite-derived and modeled oceanographic variables matched to the SDI observations. The model achieved robust predictive performance (R2 = 0.78 for training and 0.70 for testing, with RMSE = 0.31 and MAPE = 0.05 across both) and captured broad basin spatial and seasonal patterns in prokaryotic community diversity, with greater uncertainty in less-represented regions. Diversity was highest in nutrient-rich coastal areas and during winter mixing, and lowest in summer-stratified or oligotrophic waters. SHAP analysis identified photoperiod as the most significant predictor, underscoring the central role of seasonal light cycles in shaping prokaryotic community diversity. Other predictors exhibited significant season- and region-dependent effects, each contributing positively within specific environmental thresholds. Climatological diversity maps revealed consistent spatiotemporal patterns, highlighting a notable west-to-east decrease in diversity and coastal hotspots. These results demonstrate that machine learning can identify major environmental drivers of prokaryotic diversity and upscale discrete observations to basin-wide predictions. This approach is transferable to other planktonic groups and supports scalable ecosystem monitoring across environmental gradients. |
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| Keywords: | prokaryotic community diversity, machine learning, Mediterranean Sea, omics, Shannon diversity index, remote sensing |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.05.2026 |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-20 |
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| Numbering: | Vol. 95, [article no.] 103747 |
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| PID: | 20.500.12556/DiRROS-29252  |
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| UDC: | 574.5 |
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| ISSN on article: | 1878-0512 |
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| DOI: | 10.1016/j.ecoinf.2026.103747 |
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| COBISS.SI-ID: | 275422211 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 16. 4. 2026;
Soavtorji: Maria Laura Zoffoli, Pierre Ramond, Ramiro Logares, François-Yves Bouget, Pierre E. Galand, Tinkara Tinta, Neža Orel, Gianluca Volpe, Angela Landolfi, Emanuele Organelli;
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| Publication date in DiRROS: | 04.05.2026 |
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| Views: | 44 |
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| Downloads: | 23 |
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