Decoding Ecological Condition in Solomon Island Rivers with eDNA and Machine Learning

Environmental DNA (eDNA) metabarcoding offers a sensitive, non‑invasive alternative to conventional biomonitoring, yet its integration with predictive modelling remains underexplored. This study combines eDNA datasets collected during an ADB‑funded Strategic Environmental and Social Assessment (2024-2025) with supervised machine learning (ML) to evaluate ecological condition across eight rivers on Guadalcanal, Solomon Islands.eDNA samples from 47 sites identified 861 taxa at species level, spanning native forest species, disturbance‑tolerant fishes, invasive plants and animals, and microbial indicators of pollution. Classifiers (decision tree, logistic regression, random forest, eXtreme gradient boosting) were trained on species profiles contextualised by land‑use intensity and buffer zone scales (100–1000 m). Stratified 5-fold cross-validation and permutation testing ensured robust evaluation. Random forest performed best, achieving AUCs up to 0.94 ± 0.04 (p