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

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 directly compared multiple workflow components, including four DNA extraction methods, two primer sets, three Nanopore basecalling models, and two demultiplexing pipelines. Across 48 workflow combinations tested in an aquarium with 15 fish species, we mapped trade-offs between cost, sensitivity, and processing speed to assess where time and resource savings are possible without compromising detection. Workflows using the Qiagen Blood and Tissue (BT) extraction kit and amplification using the MiFish-U primer set provided the highest sensitivity, detecting ≥ 12 of 15 species by ~3-5 h and reaching the 15-OTU plateau at ~8-10 h with Oxford Nanopore's high accuracy (HAC) basecalling model. Chelex, an alternative lower-cost extraction method, showed partial recovery only (≤ 9 OTUs by 61 h) even with extended sequencing, and did not recover all 15 OTUs. DirectPCR and QuickExtract offered field-friendly extraction alternatives that achieved comparable recovery in ~10-12 h, 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 h) 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 prioritising cost, speed, and sensitivity in eDNA applications, supporting scalable, cost-effective eDNA monitoring in resource-limited settings.