Quantitative and qualitative assessment of pollen DNA metabarcoding using constructed species mixtures

Abstract

Pollen DNA metabarcoding-marker-based genetic identification of potentially mixed-species pollen samples-has applications across a variety of fields. While basic species-level pollen identification using standard DNA barcode markers is established, the extent to which metabarcoding (a) correctly assigns species identities to mixes (qualitative matching) and (b) generates sequence reads proportionally to their relative abundance in a sample (quantitative matching) is unclear, as these have not been assessed relative to known standards. We tested the quantitative and qualitative robustness of metabarcoding in constructed pollen mixtures varying in species richness (1-9 species), taxonomic relatedness (within genera to across class) and rarity (5%-100% of grains), using Illumina MiSeq with the markers rbcL and ITS2. Qualitatively, species composition determinations were largely correct, but false positives and negatives occurred. False negatives were typically driven by lack of a barcode gap or rarity in a sample. Species richness and taxonomic relatedness, however, did not strongly impact correct determinations. False positives were likely driven by contamination, chimeric sequences and/or misidentification by the bioinformatics pipeline. Quantitatively, the proportion of reads for each species was only weakly correlated with its relative abundance, in contrast to suggestions from some other studies. Quantitative mismatches are not correctable by consistent scaling factors, but instead are context-dependent on the other species present in a sample. Together, our results show that metabarcoding is largely robust for determining pollen presence/absence but that sequence reads should not be used to infer relative abundance of pollen grains.

Pollinator specialization increases with a decrease in a mass‐flowering plant in networks inferred from DNA metabarcoding

“…First, a plant species–molecular marker interaction is often observed during PCR, so that a species may be properly amplified by one marker but not by another one. Such amplification failures may lead to underrepresentation of the species concerned in sequencing products and to subsequent proportional over‐representation of the other species (Bell et al, ), thus increasing specialization level in networks. In our study, the risk of species underrepresentation (and hence the risk of proportional over‐representation of other species) was limited since we combined two markers and, for every plant species, only kept the results of the marker whose amplification was the most successful (Pornon et al, ).…”

“…There is now enough evidence that metabarcoding is a reliable way to characterize species composition in environmental DNA samples (Ji et al, 2013) including pollen samples (Bell et al, 2018).…”

“…Some studies provided evidence for the quantitative potential of pollen metabarcoding both with nuclear (ITS) and plastid markers (rbcL, trnL, trnH;Keller et al, 2015;Kraaijeveld et al, 2015;Pornon et al, 2016;Richardson et al, 2019;Smart et al, 2017). However, Bell et al (2018) andRichardson et al (2019) obtained less reliable results with ITS2. Furthermore, depending on the species, higher or lower proportions of sequence reads may be found than expected (Bell et al, 2018).…”

Introduction: Special issue on species interactions, ecological networks and community dynamics – Untangling the entangled bank using molecular techniques

“…When combined, they draw a perhaps more optimistic picture of the information value included in the read counts than contributions to the Special Issue four years ago (cf. Bell et al, 2019). Yet, each stress that for a balanced view, all sources of bias need to be considered and the methods used should be justified (Deagle et al, 2019;Piñol et al, 2019;Lamb et al, 2019).…”

“…Yet, each stress that for a balanced view, all sources of bias need to be considered and the methods used should be justified (Deagle et al, 2019;Piñol et al, 2019;Lamb et al, 2019). Stronger biases at particular steps of sample preparation, PCR and DNA sequencing will still blur correlations between original sample and relative sequence contents (Bell et al, 2019;Lamb et al, 2019).…”