DNA metabarcoding from sample fixative as a quick and voucher-preserving biodiversity assessment method
Bibliographic record
Abstract
Metabarcoding is a powerful, increasingly popular tool for biodiversity assessment, but it still suffers from some drawbacks (specimen destruction, separation, and size sorting). In the present study, we tested a non-destructive protocol that excludes any sample sorting, where the ethanol used for sample preserving is filtered and DNA is extracted from the filter for subsequent DNA metabarcoding. When tested on macroinvertebrate mock communities, the method was widely successful but was unable to reliably detect mollusc taxa. Three different protocols (no treatment, shaking, and freezing) were successfully applied to increase DNA release to the fixative. The protocols resulted in similar success in taxa detection (6.8-7 taxa) but differences in read numbers assigned to taxa of interest (33.8%-93.7%). In comparison to conventional bulk sample metabarcoding of environmental samples, taxa with pronounced exoskeleton and small-bodied taxa were especially underrepresented in ethanol samples. For EPT (Ephemeroptera, Plecoptera, Trichoptera) taxa, which are important for determining stream ecological status, the methods detected 46 OTUs in common, with only 4 unique to the ethanol samples and 10 to the bulk samples. These results indicate that fixative-based metabarcoding is a non-destructive, time-saving alternative for biodiversity assessments focussing on taxa used for ecological status determination. However, for a comprehensive assessment on total invertebrate biodiversity, the method may not be sufficient, and conventional bulk sample metabarcoding should be applied.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.002 |
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".