Metabarcoding a diverse arthropod mock community
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".