Environmental bulk samples often contain many taxa that vary several orders of magnitude in biomass. This can be problematic in DNA metabarcoding and metagenomic high-throughput sequencing approaches, as large specimens contribute disproportionately high amounts of DNA template. Thus, a few specimens of high biomass will dominate the dataset, potentially leading to smaller specimens remaining undetected. Sorting of samples by specimen size and balancing the amounts of tissue used per size fraction should improve detection rates, but this approach has not been systematically tested. Here we explored the effects of size sorting on taxa detection using two freshwater macroinvertebrate monitoring samples, collected from a low-mountain stream in Germany. Specimens were morphologically identified and sorted into three size classes (body size < 2.5x5, 5x10 and up to 10x20 mm). Tissue from each size category was extracted individually, and pooled to simulate samples that were not sorted by biomass ("Unsorted"). Additionally, size fractions were pooled so that each specimen contributed approximately equal amounts of biomass ("Sorted"). Mock samples were amplified using four different DNA metabarcoding primer sets targeting the Cytochrome c oxidase I (COI) gene. Sorting taxa by size and pooling them proportionately according to their abundance lead to a more equal amplification of taxa compared to the processing of complete samples without sorting. The sorted samples recovered 30% more taxa than the unsorted samples, at the same sequencing depth. Our results imply that sequencing depth can be decreased approximately five-fold when sorting the samples into three size classes and pooling by specimen abundance. Our study demonstrates that even a coarse size sorting can substantially improve taxa detection using DNA metabarcoding. While high throughput sequencing will become more accessible and cheaper within the next years, sorting bulk samples by specimen biomass is a simple yet efficient method to reduce current sequencing costs.
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Elbrecht et al. (2017) studied this question.