Fast, Flexible, Feasible: A Transparent Framework for Evaluating eDNA Workflow Trade-offs in Resource-Limited Settings

Environmental DNA (eDNA) analysis enables biodiversity monitoring by detecting organisms from trace genetic material, but high reagent costs, cold-chain logistics, and computational demands limit its broader use, particularly in resource-limited settings. To address these challenges and improve accessibility, we systematically evaluated multiple workflow components, including four DNA extraction methods, two primer sets, three Nanopore basecalling models, and two demultiplexing pipelines. Across 144 workflow combinations tested in an aquarium with 15 fish species, we mapped trade-offs between cost, sensitivity, and processing speed using a hierarchical Bayesian model, to assess where time and resource savings are possible without compromising detection. Workflows using the Qiagen Blood and Tissue (BT) extraction kit provided the highest sensitivity, recovering all 15 species of the sampled fish community within 3–5 hours of Oxford Nanopore sequencing when paired with MiFish-U primer set and high-accuracy (HAC) basecalling. Chelex, an alternative lower-cost extraction method, required extended sequencing (>24 hours) to reach comparable species-detection rates. DirectPCR and QuickExtract offered field-friendly extraction alternatives that achieved comparable recovery in ∼10–12 hours, though their cost-effectiveness varied. While the MarVer1 primer was designed to broaden vertebrate detection, it recovered the same fish species as MiFish-U, though with fewer total reads. Real-time sequencing trials (0–61 hours) revealed that high-efficiency workflows (BT + HAC) reached detection plateaus rapidly, indicating sequencing time can be reduced without sacrificing accuracy. The OBITools4 bioinformatics pipeline enabled automated demultiplexing but discarded more reads than an alternative, ONTbarcoder2.3, which retained low-abundance taxa at the cost of manual curation. Rather than identifying a single “best” workflow, this study provides a transparent decision framework for prioritizing cost, speed, and sensitivity in eDNA applications, supporting scalable, cost-effective eDNA monitoring, in resource-limited settings.