How quantitative is metabarcoding: A meta‐analytical approach

Abstract

Metabarcoding has been used in a range of ecological applications such as taxonomic assignment, dietary analysis and the analysis of environmental DNA. However, after a decade of use in these applications there is little consensus on the extent to which proportions of reads generated corresponds to the original proportions of species in a community. To quantify our current understanding, we conducted a structured review and meta‐analysis. The analysis suggests that a weak quantitative relationship may exist between the biomass and sequences produced (slope = 0.52 ± 0.34, p < 0.01), albeit with a large degree of uncertainty. None of the tested moderators, sequencing platform type, the number of species used in a trial or the source of DNA, were able to explain the variance. Our current understanding of the factors affecting the quantitative performance of metabarcoding is still limited: additional research is required before metabarcoding can be confidently utilized for quantitative applications. Until then, we advocate the inclusion of mock communities when metabarcoding as this facilitates direct assessment of the quantitative ability of any given study.

A guide to the application of Hill numbers to DNA‐based diversity analyses

“…Zero‐inflation is not the only issue of molecular diversity analyses though. Recently conducted research has clearly documented how many biological and technical distortion factors introduce numerous biases that break the relation between the actual biomass distribution in the system and the relative amount of DNA sequences obtained in the final results (Barnes & Turner, ; Lamb et al, ). This is partly due to primer amplification biases, and thus sequencing probability, due to the different binding affinity between primers and target sequences (Piñol, Senar, & Symondson, ).…”

“…In molecularly characterized systems, the issue of detectability is even more complex, because in addition to the biological distortions of environmental DNA (Barnes & Turner, ), there is another important source of bias produced by uneven primer amplification rates (Piñol et al, ). While eDNA representativeness assessment might be too complex to model (Alberdi et al, ; Barnes & Turner, ), amplification biases can be measured in silico (Piñol et al, ) and using mock communities (Lamb et al, ). This enables implementing occupancy‐modelling approaches that account for the relative amplification probability of OTUs (Ficetola et al, ), which have also been implemented within the framework of Hill numbers (Broms, Hooten, & Fitzpatrick, ; Iknayan, Tingley, Furnas, & Beissinger, ).…”

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

“…Metabarcoding has already been shown in several studies to successfully identify plant taxa in pollen mixtures (Cornman, Otto, Iwanowicz, & Pettis, 2015;Galimberti et al, 2014;Galliot et al, 2017;Lucas et al, 2018;Macgregor et al, 2019;Pornon et al, 2016;Richardson et al, 2019) at a higher taxonomic resolution than microscopy (Vamosi, Gong, Adamowicz, & Packer, 2017). A recent meta-analysis (Lamb et al, 2018) concluded on a significant quantitative positive linear relationship between the proportion of sequence reads and the proportion of species in original mixtures from very different origins (stomach contents, feces, airborne pollen, etc.). 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).…”

Genome‐skimming provides accurate quantification for pollen mixtures

“…Although some studies have suggested that metabarcoding is able to estimate valid abundances for pollen mixtures using amplicon frequencies (Pornon et al, ), others have shown less reliable correlations (Keller et al, ; Lamb et al, ; Richardson et al, ). These conflicting observations may imply that the success of amplicon‐based metabarcoding is dependent on species composition of the pollen sample, which is highly variable in natural systems.…”