Multi-scale machine learning identifies indicator species from environmental-DNA co-occurrence networks

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Abstract
Environmental DNA (eDNA) metabarcoding produces sparse read-count matrices routinely binarised to presence/absence to mitigate amplification, copy-number and shedding biases. We argue that binary eDNA data are sufficient to recover detailed community structure, sparse indicator panels and habitat-specific co-occurrence sub-networks — given the right analytical pipeline. Yet integrative frameworks bridging taxon-level and community-level structure remain missing for macro-organism eDNA. We propose a reproducible framework combining three scales: sparse machine-learning indicator-species identification (Predomics), consensus co-occurrence network inference (ScaleNet), and module-level enrichment via Gene Set Enrichment Analysis (GSEA). We applied it to 217 eDNA samples spanning eight habitats and three depth zones (shallow, <50 m; middle, 50–250 m; deep, 200–600 m) in New Caledonia, South Pacific. A sparse 55-MOTU indicator panel captured a proportion of beta-diversity variance comparable to the full 318-MOTU community. ScaleNet reconstructed a 261-node, 579-edge network with a core-periphery main component (shallow in the core, middle at the periphery) and a structurally isolated deep-zone subgraph. Fast-Greedy modularity partitioned it into 13 modules, 11 significantly enriched for depth zones or habitats by GSEA, each mapping onto a coherent trophic guild — from mesopelagic lanternfishes over deep seamounts to reef grazers across shallow microhabitats. Indicator taxa occupied structurally peripheral positions while connectivity hubs were non-indicator generalists, with reef-scraping parrotfish a functionally important exception. Sparse indicator panels efficiently capture zone discrimination while co-occurrence networks reveal community-level integration — together providing a scalable, interpretable framework for eDNA-based marine biodiversity monitoring.

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