Metabarcoding a diverse arthropod mock community

Abstract

Although DNA metabarcoding is an attractive approach for monitoring biodiversity, it is often difficult to detect all the species present in a bulk sample. In particular, sequence recovery for a given species depends on its biomass and mitome copy number as well as the primer set employed for PCR. To examine these variables, we constructed a mock community of terrestrial arthropods comprised of 374 species. We used this community to examine how species recovery was impacted when amplicon pools were constructed in four ways. The first two protocols involved the construction of bulk DNA extracts from different body segments (Bulk Abdomen, Bulk Leg). The other protocols involved the production of DNA extracts from single legs which were then merged prior to PCR (Composite Leg) or PCR‐amplified separately (Single Leg) and then pooled. The amplicons generated by these four treatments were then sequenced on three platforms (Illumina MiSeq, Ion Torrent PGM and Ion Torrent S5). The choice of sequencing platform did not substantially influence species recovery, although the Miseq delivered the highest sequence quality. As expected, species recovery was most efficient from the Single Leg treatment because amplicon abundance varied little among taxa. Among the three treatments where PCR occurred after pooling, the Bulk Abdomen treatment produced a more uniform read abundance than the Bulk Leg or Composite Leg treatment. Primer choice also influenced species recovery and evenness. Our results reveal how variation in protocols can have substantial impacts on perceived diversity unless sequencing coverage is sufficient to reach an asymptote.

Metabarcoding a Metacommunity: detecting change in a wetland wilderness

“…A key difference between our sampling approaches was that the standard CABIN wetland protocol (19) provided estimates of relative abundance based on counts from a subset of each sample, whereas sequences identified using DNA metabarcoding were converted to presence-absence data (13, 28). In addition, CABIN identified 74 families based on morphological features, but metabarcoding could identify 109 families, as well as 263 genera (see Supplement 1 and Fig.…”

“…We also used the occupancy model framework to compare detectability of each taxon with different primers, a more robust measure of their complementarity than lists of taxa observed. Quantifying detectability is vital to making the results of this study comparable to others with varying protocols, and this approach could be used to refine and select complementary primers (28, 31). Crucially, DNA metabarcoding, particularly at genus-level, substantially improved our power to detect ecosystem-scale changes compared to traditional CABIN sampling (Fig.…”

In silicoand empirical evaluation of twelve COI & 16S metabarcoding primer sets for insectivorous diet analyses

“…Indeed, in our study combining both in silico , mock community and guano analyses enabled us to reveal both the strong and weak points of 12 primer sets for a large spectrum of taxa. DNA quality is not of importance during in silico and mock community analyses, so that the amplicon length has no significant effect on the number of arthropod taxa detected, while the degeneracy level of the primers has a major positive effect as it minimizes mismatches for DNA from diverse taxonomic assemblages (Braukmann et al, 2019). On the other hand, when considering DNA from guano samples, amplicon length was the most important factor influencing the success of arthropod detection.…”

Understanding PCR Processes to Draw Meaningful Conclusions from Environmental DNA Studies

“…Because each taxon (or, more broadly, template molecule) has its own amplification efficiency for a given set of primers, we describe three simulations according to the distribution of these efficiency parameters among the taxa in a community ( Figure 2). We treat biases arising from sequence variation or from secondary structure ("polymerase bias"; see e.g., [25]) as equivalent for the present purposes. For all scenarios, we use the same relationship for translating among-taxon variation in amplification efficiency into the number of amplicons observed for taxon i, A i , at the conclusion of the PCR.…”

“…We drew empirical data from published metabarcoding papers to parameterize our models, finding several recent papers [27,1,6,28,29,25] that reported results from mock (i.e., synthetic) eDNA communities useful for our purposes. These papers provided the number of PCR cycles used, the starting concentrations of DNA for a variety of taxa, the primers used, and the ending counts of amplicon reads; such data allowed us to calculate taxon-specific amplification efficiencies for each primer set (Supp.…”