DIVE: Diversity-Informed Valuation of Ecosystem Functioning

Meta-omics datasets are inherently high-dimensional, complicating their interpretation and limiting their application to ecological and ecosystem-level analyses. We present DIVE (Diversity-Informed Valuation of Ecosystem Functioning), a structured and reproducible workflow that aggregates high-dimensional meta-omics data into ecologically meaningful community-level descriptors for investigating biodiversity–ecosystem functioning (BEF) relationships. Rather than introducing new diversity metrics or statistical estimators, DIVE integrates established alpha-diversity measures with sampling-design-aware temporal association analysis into a unified framework for relating microbial community structure to ecosystem function. The workflow is demonstrated using a multi-year metatranscriptomics dataset from a full-scale activated sludge wastewater treatment plant. DIVE identified a coherent temporal pattern approximately 23 days before ecosystem-functioning measurements, with richness-, diversity-, and evenness-related descriptors showing positive associations and dominance-related descriptors showing associations in the opposite direction. These relationships are interpreted as exploratory biodiversity–function associations rather than evidence of causation or validated prediction. By formalizing the integration of established analytical approaches into a temporally explicit workflow, DIVE supports reproducible and ecologically interpretable BEF analyses of longitudinal meta-omics data and prioritization of candidate relationships for further mechanistic investigation.