Explainable AI for Marine Ecological Quality Prediction: Integrating Microbiome Data, Metadata, and Diversity
Résumé
Assessing ecological quality (EQ) is crucial for marine biodiversity monitoring. With the advent of High-Throughput Sequencing technologies, metabarcoding has enabled large-scale microbial community analysis through Operational Taxonomic Unit (OTU) tables, providing an alternative for EQ assessment. Machine learning (ML) models have been successfully applied for this task, but they often treat microbial abundance as the sole predictor, overlooking environmental meta-data (e.g., pH, salinity, temperature) and diversity indices (alpha and beta diversity). This study integrates metadata and diversity indices into an explainable ML framework for EQ prediction. Using SHapley Additive Explanations (SHAP), we assess the contribution of these features to model predictions across five genetic markers (V1V2, V3V4, V4, 37F, and V9). Our results highlight marker-dependent feature importance, demonstrating that while OTU-based models remain dominant, incorporating metadata improves accuracy for certain markers. This work enhances interpretability in AI-driven biomonitoring, fostering more reliable marine ecosystem assessments.
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